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Part 01-Module 01-Lesson 06_Data Processing/14. Eddy Deven V2-g7zJV-Ontbo.mp4 59.87 MB
Part 02-Module 01-Lesson 01_Welcome To Term II/04. NewsRoom Liz V2-Os881EhJv68.mp4 56.2 MB
Part 02-Module 01-Lesson 01_Welcome To Term II/05. AITND Term II Interview W Justin V2 V2-JOkwa1brNX8.mp4 48.24 MB
Part 01-Module 01-Lesson 09_Project 1 Trading with Momentum/04. Eddy Thomas 02 AI And Finanace V2-8Hna_hR_N7c.mp4 37.01 MB
Part 02-Module 01-Lesson 05_Financial Statements/03. M5 SC 15 10Ks Walkthrough V1-0ytyZ4LVG6s.mp4 36.87 MB
Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/05. Eddy Thomas 03 Jobs And Interviews V2-5W9XsgyxYHE.mp4 33.77 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/25. Kalman Filter Code Solution - Artificial Intelligence for Robotics-X7cixvcogl8.mp4 33.73 MB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.mp4 32.54 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/24. PyTorch - Part 8-S9F7MtJ5jls.mp4 29.51 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/16. NewsRoom Juan V2-DpO73QaXaOY.mp4 25.94 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/06. AIT M5L5 05 Frequency Reweighting V2-X-Cf2Uj7tH4.mp4 25.33 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.mp4 25.04 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/10. NewsRoom Liz Short V2-vqHsk3hjBLU.mp4 24.61 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/12. Eddy Thomas 01 Quants V2-ZRzhyaqz7I0.mp4 23.82 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.mp4 23.75 MB
Part 01-Module 04-Lesson 01_Factors/12. Zipline Pipeline SC V1-DHTwIwVk_sc.mp4 23.46 MB
Part 07-Module 01-Lesson 03_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 23.31 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/12. L4 13 Limitations V2-UbbZa7-3iuk.mp4 23.2 MB
Part 02-Module 05-Lesson 04_Project 8 Backtesting/03. AITND Finish V2-g-a4ZNIPQqw.mp4 23.1 MB
Part 02-Module 03-Lesson 05_Feature Engineering/10. M7L5 22 V1-34bV5mYUJAI.mp4 22.67 MB
Part 02-Module 01-Lesson 05_Financial Statements/12. M5 SC 7 Metacharacters Part 2 V1-KK1xo8GDfvE.mp4 22.56 MB
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.mp4 22.05 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 18 V1-7LESwsbt70s.mp4 21.98 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.mp4 21.97 MB
Part 02-Module 03-Lesson 02_Decision Trees/02. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 21.68 MB
Part 07-Module 01-Lesson 04_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 21.68 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.mp4 21.62 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/03. M8L1 07 Backtest Validity V5-KTlrel9p6Q0.mp4 21.52 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/12. PyTorch V2 Part 3 Solution V2-zBWlOeX2sQM.mp4 21.27 MB
Part 07-Module 01-Lesson 02_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 21.06 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.mp4 20.96 MB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/09. Linear Transformations 1-99jYIxBRDww.mp4 20.77 MB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.mp4 20.63 MB
Part 02-Module 01-Lesson 05_Financial Statements/02. AIT M5L4A 02 Financial Statement V6-XYff0ROHzWo.mp4 20.45 MB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/11. Linear Transformations 3-g_yTyRwMzXU.mp4 20.09 MB
Part 03-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.mp4 19.91 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/18. MV When You Dont Believe In Yourself 1 V1-rjCr-Z7UhZE.mp4 19.84 MB
Part 01-Module 04-Lesson 01_Factors/02. M4 L1A 02 Intro V2-W7_llXQ2GhA.mp4 19.76 MB
Part 02-Module 01-Lesson 03_Text Processing/06. Cleaning-qawXp9DPV6I.mp4 19.59 MB
Part 02-Module 05-Lesson 04_Project 8 Backtesting/01. NewsRoom Eddy 1 V2-9MlGvKWQn_o.mp4 19.26 MB
Part 03-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.mp4 18.95 MB
Part 01-Module 01-Lesson 06_Data Processing/11. M1L4 13 Exchange Traded Funds V4-Zx7v5GCfpvI.mp4 18.91 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/11. PyTorch V2 Part 3 V1-9ILiZwbi9dA.mp4 18.8 MB
Part 03-Module 01-Lesson 03_Control Flow/10. For Loops-UtX0PXSUCdY.mp4 18.44 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/20. 07 CharRNN Solution V1-ed33qePHrJM.mp4 18.32 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/17. MV 12 Embrace The Struggle V2-SGcgOm5kiKU.mp4 18.31 MB
Part 03-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.mp4 18.28 MB
Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.mp4 18.01 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/08. Jonathan Larkin Careers-QhHNPxM_Ku4.mp4 17.97 MB
Part 02-Module 01-Lesson 05_Financial Statements/13. M5 SC 8 Metacharacters Part 3 V1-nDlxRlDUNHk.mp4 17.63 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/02. AIT M5L5 02 Readability V2-pcB63lIgiQg.mp4 17.62 MB
Part 02-Module 01-Lesson 05_Financial Statements/14. M5 SC 9 Substitutions And Flags V1-9pxTGOlkLEY.mp4 17.51 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/25. PyTorch V2 Part 8 Solution V1-4n6T93hKRD4.mp4 17.49 MB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.mp4 17.37 MB
Part 03-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.mp4 17.37 MB
Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 6 V1-R8kfSvnHA3k.mp4 17.37 MB
Part 02-Module 01-Lesson 01_Welcome To Term II/01. AITND TII 01 Recap Of Term 1 V1-uhIvBfhcyLM.mp4 17.3 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/13. Strings-ySZDrs-nNqg.mp4 17.26 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/07. M4 L1B 06 Factor Models In Quant Finance V2-VeM2SudgZqc.mp4 17.25 MB
Part 03-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.mp4 16.99 MB
Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 12 V1-cHU7Sh12eOo.mp4 16.88 MB
Part 06-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.mp4 16.86 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.mp4 16.81 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/10. PyTorch V2 Part 2 Solution 2 V1-8KRX7HvqfP0.mp4 16.7 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/04. M2L4 05 Advanced Time Series Models V5-cj1RCBTDog8.mp4 16.63 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.mp4 15.95 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/19. Saving Loading Models V1-psmrPu-mseA.mp4 15.94 MB
Part 03-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.mp4 15.93 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/17. PyTorch V2 Part 5 Solution V1-AjrXltxqsK4.mp4 15.84 MB
Part 06-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.mp4 15.78 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/17. 04 Implementing CharRNN V2-MMtgZXzFB10.mp4 15.77 MB
Part 02-Module 03-Lesson 07_Feature Importance/07. L7 HS17 V1-k0vANVo-5Ek.mp4 15.71 MB
Part 03-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.mp4 15.71 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/05. PyTorch V2 Part 1 Solution V1-mNJ8CujTtpo.mp4 15.66 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/11. L4 12 Rebalancing Strategies V2-8u5gBx-fYr8.mp4 15.62 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/26. M4 L1B 25 Other Alternative Data V1-hMw3AuPVSSs.mp4 15.49 MB
Part 03-Module 01-Lesson 04_Functions/02. Defining Functions-IP_tJYhynbc.mp4 15.48 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/08. Números inteiros e floats-MiJ1vfWp-Ts.mp4 15.41 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.mp4 15.33 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/08. M4 L1B 07 Risk Factors V Alpha Factors V2-9KUpH1MDC1k.mp4 15.23 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/14. PCA Toy Problem SC V1-uyl44T12yU8.mp4 15.15 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/02. M2L2 02 Sources Of Outliers V8-gXKhKQ2_TaA.mp4 15.13 MB
Part 01-Module 02-Lesson 05_Volatility/12. M2L5 12 Using Volatility For Equity Trading V5-Vh9ajVRedvY.mp4 15.13 MB
Part 02-Module 01-Lesson 05_Financial Statements/18. M5 SC 16 HTML Structure V1-R3QLtHxedXw.mp4 15.07 MB
Part 03-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.mp4 15.06 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/25. M4 L1B 24 NLP Used To Enhance Fundamental Analysis V1-9zMWuZ9j7rI.mp4 15.06 MB
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.mp4 14.97 MB
Part 01-Module 04-Lesson 06_Alpha Factors/53. M4 L3a 27 Interlude Pt 3 V2-v6cLkoJhujU.mp4 14.8 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/12. 02 Time Series Prediction V2-xV5jHLFfJbQ.mp4 14.8 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/02. M8L1 06 What Is A Backtest V4-q2dW6-ZRaXE.mp4 14.77 MB
Part 01-Module 04-Lesson 06_Alpha Factors/52. M4 L3a 26 Interlude Pt 2 V2-1a60RPqhO8k.mp4 14.72 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/06. 4 Data Subsampling V1-7SJXv2BQzZA.mp4 14.69 MB
Part 02-Module 01-Lesson 05_Financial Statements/09. M5 SC 4 Searching For Simple Patte V1-7RAHoJ34gXI.mp4 14.65 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/20. PyTorch - Part 7-hFu7GTfRWks.mp4 14.62 MB
Part 07-Module 01-Lesson 02_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 14.35 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/16. 12 CompleteModel CustomLoss V2-7SqNN_eUAdc.mp4 14.35 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/03. M4 L1B 03 Factor Returns As Latent Variables V3-LpHvJq6XTOQ.mp4 14.25 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/02. Jonathan Larkin - What Is A Quant-G22oM0qv0Hs.mp4 14.19 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/10. L4 11 Rebalancing A Portfolio V2-S5SPhBpG3b0.mp4 14.19 MB
Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 9 V1-x7ON_-QrpHQ.mp4 14.18 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/02. M4 L1B 02 What Is A Factor Model V4-K5QKPwU38Do.mp4 14.18 MB
Part 05-Module 01-Lesson 02_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.mp4 14.18 MB
Part 04-Module 01-Lesson 03_Linear Combination/02. Linear Combinations 2-RsKJNDTb8nw.mp4 14.17 MB
Part 01-Module 01-Lesson 06_Data Processing/05. M1L4 08 Missing Values V5-XaMaVFUIc_I.mp4 14.17 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/10. AffdexMe Demo-dpFtXDqakvY.mp4 13.96 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/06. Vision-based Emotion AI-7nKKWWn1sAc.mp4 13.76 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/11. 9 Model Validation Loss V2-GKDCq8J76tM.mp4 13.67 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/04. M8L1 08 Backtest Overfitting V2-MnWHGIiqjns.mp4 13.63 MB
Part 02-Module 01-Lesson 05_Financial Statements/10. M5 SC 5 Word Boundaries V1-3dWIHULqKog.mp4 13.61 MB
Part 01-Module 04-Lesson 01_Factors/01. M4 L3A 01 Intro To The Factors V2-OqhRUxHf6wo.mp4 13.54 MB
Part 01-Module 04-Lesson 06_Alpha Factors/05. M4 L3a 04 Researching Alphas From Academic Papers V4-te0UTxemLBE.mp4 13.52 MB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/10. Linear Transformations 2-imtEd8M6__s.mp4 13.49 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/25. L2 06 Lists Methods V1-tz2Ja1Eaeqo.mp4 13.33 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.mp4 13.28 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.mp4 13.27 MB
Part 03-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.mp4 13.22 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.17 MB
Part 02-Module 03-Lesson 02_Decision Trees/17. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.14 MB
Part 07-Module 01-Lesson 04_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.14 MB
Part 02-Module 01-Lesson 05_Financial Statements/23. M5 SC 14 Searching The Parse Tree Part 3 V1-PR--1dLqcTM.mp4 13.06 MB
Part 01-Module 04-Lesson 06_Alpha Factors/50. M4 L3a 23 Summary V3-FZYqdaqoiZk.mp4 12.98 MB
Part 01-Module 04-Lesson 06_Alpha Factors/34. M4 L3a 151 The Fundamental Law Of Active Management Part 1 V4-iCW_vqvrTlw.mp4 12.96 MB
Part 06-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.mp4 12.85 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/16. PyTorch V2 Part 5 V1 (1)-XACXlkIdS7Y.mp4 12.81 MB
Part 03-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.mp4 12.78 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 51 Shap One Solution V2 (1)-Q0qAsUz2gnU.mp4 12.77 MB
Part 07-Module 01-Lesson 03_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 12.68 MB
Part 02-Module 01-Lesson 05_Financial Statements/16. AIT M5L4B 06 Introduction To Beautifulsoup V3-k8e-kB3qBng.mp4 12.67 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/16. M4 L1B 15 Volume Factors V1-1dTAV3Irxv4.mp4 12.63 MB
Part 02-Module 03-Lesson 02_Decision Trees/09. Entropy-piLpj1V1HEk.mp4 12.59 MB
Part 07-Module 01-Lesson 04_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 12.59 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.mp4 12.55 MB
Part 06-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.mp4 12.52 MB
Part 01-Module 02-Lesson 05_Volatility/11. M2L5 11 Markets Volatility V3-jEHJkZUX9s4.mp4 12.46 MB
Part 02-Module 03-Lesson 05_Feature Engineering/11. M7L5 28 V1-Ffax3lTKAs0.mp4 12.45 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/21. 08 Making Predictions V3-BhrpV3kwATo.mp4 12.38 MB
Part 02-Module 03-Lesson 02_Decision Trees/11. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.34 MB
Part 07-Module 01-Lesson 04_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.34 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/04. PyTorch V2 Part 1 V1-6Z7WntXays8.mp4 12.32 MB
Part 01-Module 03-Lesson 02_ETFs/09. L2 11 2 Arbitrage Farmers Market V1-hHxp16mQNGA.mp4 12.22 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/22. M4 L1B 21 Analyst Ratings V1-cHkJo8qBKes.mp4 12.18 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.mp4 11.95 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.mp4 11.94 MB
Part 02-Module 01-Lesson 05_Financial Statements/21. M5 SC 12 Searching The Parse Tree Part 1 V1-RyJuvYTF3Ms.mp4 11.88 MB
Part 02-Module 01-Lesson 05_Financial Statements/07. M5 SC 2 Finding Words V1-wTOh9B6aHGk.mp4 11.78 MB
Part 01-Module 04-Lesson 06_Alpha Factors/24. M4 L3a 12 Return Denominator Leverage And Factor Returns V3-QxHrP5LoXAI.mp4 11.73 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/14. M4 L4 17 Path Dependency 1 V3-ok9rKYRtZLE.mp4 11.6 MB
Part 06-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.mp4 11.54 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/15. M4 L1B 14 PriceVolume Factors V2-zaG0PDc3wsA.mp4 11.54 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.mp4 11.48 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/07. M8L1 14 Structural Changes V1-EaxepBSycbQ.mp4 11.47 MB
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.mp4 11.42 MB
Part 03-Module 01-Lesson 05_Scripting/20. Importing Files-qjeSn6zZbR0.mp4 11.41 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/10. 8 Word2vec Model V2-7BEYWhym8lI.mp4 11.35 MB
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.mp4 11.32 MB
Part 01-Module 04-Lesson 06_Alpha Factors/30. M4 L3A 141 Ranked Information Coefficient Part 1 V4-_huNulOIuB0.mp4 11.32 MB
Part 02-Module 01-Lesson 05_Financial Statements/05. AIT M5L4B 01 Introduction To Regex V4-WCXDD_n1ZuA.mp4 11.27 MB
Part 01-Module 02-Lesson 03_Regression/06. Testing For Normalilty-Sa1MJegyYfc.mp4 11.27 MB
Part 06-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.mp4 11.26 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/11. L3 09 Capital Market Line V2-BRO-vo3y0-U.mp4 11.25 MB
Part 06-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.mp4 11.22 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/04. 04. Computer Vision Applications-aFJKp2NltCY.mp4 11.19 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.mp4 11.18 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/12. M4 L3b 09 Winners And Losers Creating A Joint Factor V3-xmW05ii8Vxs.mp4 11.15 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/22. M4 L3b 18 IVol Value Fundamental Or Discretionary Investing V2-sKAE5Z8e7IM.mp4 11.14 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/07. AIT M5L5 08 Similarity Analysis V3-LNro_9JOIrY.mp4 11.12 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/06. M2L4 07 Kalman Filter V4-CLJhgfMI4Ho.mp4 11.09 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.mp4 11.07 MB
Part 06-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.mp4 11.04 MB
Part 02-Module 01-Lesson 05_Financial Statements/20. M5 SC 11 Navigating The Parse Tree V1-NzOB9Vyy0l4.mp4 10.97 MB
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.mp4 10.92 MB
Part 03-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.mp4 10.89 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/05. 05. Emotional Intelligence-D_LzJsJH5qk.mp4 10.88 MB
Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.mp4 10.81 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.mp4 10.8 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/01. M4 L2A 01 Intro V1-DgsD3yL9Yy0.mp4 10.8 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.mp4 10.75 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/14. M4 L3b 10 Skewness And Momentum Attentional Bias V3-3ZkFRBUmSQ0.mp4 10.75 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.mp4 10.7 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/10. M4 L4 10 Estimation Error V4-WdrMIRhScN0.mp4 10.69 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/02. Introduction-XZL934YQ-FQ.mp4 10.68 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.mp4 10.68 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/05. M4 L3b 04 Overnight Returns Data Universe Methods V2-Y_lBDa1hRco.mp4 10.55 MB
Part 03-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.mp4 10.55 MB
Part 07-Module 01-Lesson 03_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 10.53 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/04. M2L2 03 Outliers Signals And Strategies V5-zyVgpsRy-mU.mp4 10.51 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/10. M4 L1B 09 Risk Factors V Alpha Factors Part 3 V1-UmdOVhcRCVU.mp4 10.49 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/21. M4 L1B 20 Pre And Post Event V1-Olz9QZQaBxs.mp4 10.49 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/03. M8l2 02 Barra Data Take2 V1-7WjYfvpLSTM.mp4 10.46 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/03. L4 03 Optimization With Constraints V3-91WzhG6dti8.mp4 10.43 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/04. AIT M5L5 04 BagofWords V3-8t2bf9kAVHE.mp4 10.35 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 50 Shap One Intro V1 (1)-BKD_FseE6Z4.mp4 10.3 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/11. M4 L1B 10 Risk Factors V Alpha Factors Part 4 V1-3ZE298YwbCM.mp4 10.29 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.mp4 10.23 MB
Part 01-Module 01-Lesson 09_Project 1 Trading with Momentum/01. MV 03 Transition To Project 01 V1-dcps5Bg4bZE.mp4 10.22 MB
Part 01-Module 02-Lesson 03_Regression/01. M2L3 01 Intro V4-C7vWJH05tKA.mp4 10.16 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.mp4 10.15 MB
Part 01-Module 04-Lesson 06_Alpha Factors/01. M4 L3a 01 Intro Efficient Market Hypothesis And Arbitrage Opportunities V3--YpXAt7zuh8.mp4 10.1 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/04. 3 Data PreProcessing V1-Xw1MWmql7no.mp4 10.09 MB
Part 01-Module 04-Lesson 06_Alpha Factors/46. M4 L3a 20 Transfer Coefficient V3-4rZ0MWQzlIs.mp4 10.08 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/08. PyTorch V2 Part 2 V1-CSQOdOb2mlg.mp4 10 MB
Part 01-Module 04-Lesson 06_Alpha Factors/09. M4 L3a 06 Ranking Part 1 V4-4j2hIB7WHY4.mp4 10 MB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.mp4 9.98 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 53 Additive Feature Att Part 2 V1-ah171-grqus.mp4 9.88 MB
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.mp4 9.87 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/03. M1L1 Introducing The Instructors 1 V4-l5gG7r-BWYc.mp4 9.85 MB
Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/01. MV 7 Transition To Project 02 1 V1-nkAcx2X_lfs.mp4 9.82 MB
Part 01-Module 04-Lesson 06_Alpha Factors/39. M4 L3a 172 Factor Rank Autocorrelation Turnover V2-QBvbMiVW100.mp4 9.81 MB
Part 05-Module 01-Lesson 02_NumPy/08. NumPy 4 V1-jeU7lLgyMms.mp4 9.8 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 43 Prediction Solution Part 1 V2-VgtWMV2GIic.mp4 9.79 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/03. M2L6 04 Pairs Trading V3-7lEm_tFXcBk.mp4 9.73 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Solution Part 2 V1-UhtIVQ8YGvI.mp4 9.73 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/04. M1L1 05 Program Overview V1-Ci0j_UwLlQQ.mp4 9.73 MB
Part 02-Module 03-Lesson 04_Random Forests/01. L4 01 HS Intro V2-9c5d6MvguA0.mp4 9.68 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.mp4 9.59 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/13. 03 Training Memory V1-sx7T_KP5v9I.mp4 9.57 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/01. MV 11 Intro To Module 03 Difficulties In Learning V1-kqjFkUVZwEc.mp4 9.55 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/12. 10 NegativeSampling V1-gnCwdegYNsQ.mp4 9.51 MB
Part 01-Module 03-Lesson 02_ETFs/12. MV 11 Guided Meditation V1-njp1mnEEv9s.mp4 9.45 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/13. M4 L1B 12 How An Alpha Factor Becomes A Risk Factor Part 2 V1-9waaTtRaU-Y.mp4 9.44 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.mp4 9.4 MB
Part 02-Module 01-Lesson 07_Project 5 NLP on Financial Statements/01. Intro Term II V2-jSK9Pr7wFQo.mp4 9.34 MB
Part 07-Module 01-Lesson 02_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.33 MB
Part 01-Module 04-Lesson 06_Alpha Factors/51. M4 L3a 25 Interlude Pt 1 V2-SMQwc5kwSr0.mp4 9.26 MB
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.mp4 9.25 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.mp4 9.25 MB
Part 02-Module 03-Lesson 02_Decision Trees/16. Information Gain-k9iZL53PAmw.mp4 9.24 MB
Part 07-Module 01-Lesson 04_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4 9.24 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/15. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.23 MB
Part 07-Module 01-Lesson 04_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.2 MB
Part 01-Module 04-Lesson 06_Alpha Factors/49. M4 L3a 22 Conditional Factors V2-2J1aUwGq6tc.mp4 9.17 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 24 CaseB Solution T2 V1-lNlhJhtCdxE.mp4 9.11 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/19. 06 Defining Model V2-_LWzyqq4hCY.mp4 9.05 MB
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.mp4 9.02 MB
Part 03-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.mp4 9.01 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/09. 8 TensorDataset Batching V1-Oxuf2QIPjj4.mp4 9.01 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/05. M4 L1B 04 Factor Model Assumptions V3-qEu3m_3eGWk.mp4 8.95 MB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 8.93 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 42 Prediction Intro Part 2 V1-DG-HiRum1JU.mp4 8.88 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 22 CaseA Part 2 V1-Kuwa4DkNBZs.mp4 8.88 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/24. M4 L1B 23 Sentiment Analysis On News And Social Media V1-Jph7h2Yl0yg.mp4 8.87 MB
Part 03-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.mp4 8.85 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/09. PyTorch V2 Part 2 Solution V1-zym36ihtOMY.mp4 8.78 MB
Part 07-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 8.76 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/13. M4 L2b 14 Explained Variance V3-OdHeReNUqoQ.mp4 8.69 MB
Part 02-Module 01-Lesson 05_Financial Statements/11. M5 SC 6 Metacharacters Part 1 V1-Jay3euM62NQ.mp4 8.61 MB
Part 04-Module 01-Lesson 01_Introduction/03. Essence Of Linear Algebra Intro -EHcxDZpeGFg.mp4 8.6 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.mp4 8.58 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/16. M4 L3b 12 Skewness And Momentum Momentum Enhances Or Weakened By Skew V2-S73J_h8DHsE.mp4 8.57 MB
Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/04. MV 10 Transition From Project 02 Int V1-DYjOsL3VYfY.mp4 8.51 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/07. M2L2 06 Spotting Outliers In Signal Returns V4-BSLGZz0RzTg.mp4 8.5 MB
Part 05-Module 01-Lesson 02_NumPy/07. NumPy 3 V1-Rt4aydeo9F8.mp4 8.5 MB
Part 07-Module 01-Lesson 02_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.49 MB
Part 03-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.mp4 8.37 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian - Artificial Intelligence for Robotics-fRYtUP0P4Lg.mp4 8.34 MB
Part 04-Module 01-Lesson 03_Linear Combination/01. Linear Combinations 1-fmal7UE7dEE.mp4 8.3 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.mp4 8.26 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/15. PyTorch V2 Part 4 Solution V1-R6Y4hPLVQWM.mp4 8.24 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/18. M4 L1B 17 Fundamental Ratios V2-Eo-faV9CsP8.mp4 8.24 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/15. M4 L4 19 What Is Optimization Doing To OUr Alphas V3-6Yqb91Xahvg.mp4 8.22 MB
Part 01-Module 02-Lesson 01_Quant Workflow/05. M2L1 04 Anatomy Of A Strategy Part 3 V1-vSxnkduTWWY.mp4 8.21 MB
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.mp4 8.19 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 1 V2-IXlHYPjcQ-o.mp4 8.16 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/05. M8L1 10 Overtrading V2-0cdGLRDI_Sk.mp4 8.16 MB
Part 07-Module 01-Lesson 03_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4 8.13 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/03. M4 L4 03 Setting Up The Problem Risk V4-2vcULOlXTzc.mp4 8.13 MB
Part 01-Module 02-Lesson 05_Volatility/08. M2L5 07 Exponentially Weighted Moving Average V4-VBPitTHzYRI.mp4 8.13 MB
Part 05-Module 01-Lesson 03_Pandas/12. Pandas 7 V1-ruTYp-twXO0.mp4 8.09 MB
Part 01-Module 04-Lesson 06_Alpha Factors/31. M4 L3A 142 Ranked Information Coefficient Part 2 V5-WKGmog0Nzgo.mp4 8.07 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 4 V1-l614btyXRCI.mp4 8.04 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/09. 09. Training a Model-m4GVfwVkj74.mp4 8.01 MB
Part 02-Module 03-Lesson 02_Decision Trees/12. Entropy Formula-w73JTBVeyjE.mp4 8 MB
Part 07-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8 MB
Part 03-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.mp4 7.99 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 1 V3-1lkQSp6FHgo.mp4 7.94 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/06. M2L2 05 Handling Outliers In Raw Data V3-3l6kQZqlVJA.mp4 7.92 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.mp4 7.87 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/06. M4 L4 07 Leverage Constraint V5-zJ9gon4rFQc.mp4 7.87 MB
Part 05-Module 01-Lesson 03_Pandas/10. Pandas 6 V1-GS1kj04XQcM.mp4 7.87 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 2 V1-ZxfpUIY_AcE.mp4 7.86 MB
Part 05-Module 01-Lesson 03_Pandas/09. Pandas 5 V1-lClsJnZn_7w.mp4 7.85 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/04. M2L6 07 Finding Pairs To Trade V4-6hQtoElcnGM.mp4 7.82 MB
Part 01-Module 04-Lesson 06_Alpha Factors/04. M4 L3a 03 Definition Of Key Words V4-zySdIQTPTGo.mp4 7.8 MB
Part 01-Module 02-Lesson 03_Regression/11. M2L3 10 Linear Regression V4-GRY4eakMBJ8.mp4 7.8 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/01. Welcome to Computer Vision-GgA3_-MMT_I.mp4 7.77 MB
Part 01-Module 04-Lesson 06_Alpha Factors/35. M4 L3a 152 The Fundamental Law Of Active Management Part 2 V7-CMc4ujA8Ahs.mp4 7.75 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/08. M2L4 09 Recurrent Neural Networks V5-5cYAAHyRHDo.mp4 7.74 MB
Part 06-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.mp4 7.74 MB
Part 01-Module 02-Lesson 05_Volatility/01. M2L5 01 What Is Volatility V3-brGVwpDSuG4.mp4 7.73 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 10 Node Importance Intro V1-Q7KJgKCm8cs.mp4 7.7 MB
Part 01-Module 04-Lesson 06_Alpha Factors/06. M4 L3a 051 Controlling For Risk Within An Alpha Factor Part 1 V3-raeVfAbBXnA.mp4 7.7 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/09. M4 L1B 08 Risk Factors V Alpha Factors Part 2 V2-AApfsuSpnMY.mp4 7.7 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.mp4 7.67 MB
Part 03-Module 01-Lesson 03_Control Flow/02. Indentation-G8qUNOTHtrM.mp4 7.65 MB
Part 02-Module 01-Lesson 05_Financial Statements/22. M5 SC 13 Searching The Parse Tree Part 2 V1-WS_bkGCk7qk.mp4 7.65 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.mp4 7.61 MB
Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 13 Marketvol Solution V1-yR1eWiv1jTM.mp4 7.61 MB
Part 01-Module 04-Lesson 06_Alpha Factors/36. M4 L3a 161 Real World Constraints Liquidity V3-eu0YZRMu_3w.mp4 7.61 MB
Part 01-Module 01-Lesson 05_Market Mechanics/02. M1L3 02 Farmers Market V3-i_itXOdetCc.mp4 7.58 MB
Part 01-Module 03-Lesson 02_ETFs/06. L2 08 Authorized Participant And The Create Process V4-u4thSf3Uxsc.mp4 7.57 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/07. Regularization-ndYnUrx8xvs.mp4 7.57 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.54 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.54 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/11. M4 L4 12 Infeasible Problems V4-ljg25Rj511Q.mp4 7.53 MB
Part 05-Module 01-Lesson 02_NumPy/04. NumPy 1 V1-EOHW29kDg7w.mp4 7.53 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.mp4 7.52 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.mp4 7.49 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.mp4 7.48 MB
Part 01-Module 04-Lesson 06_Alpha Factors/13. M4 L3a 08 Z Score V3-6_cKCoLa92o.mp4 7.47 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/24. M4 L3b 20 IVol Volatility Enhanced Price Earnings Ratio V2-x-1nqTEPGcA.mp4 7.46 MB
Part 03-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.mp4 7.42 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 20 Shap Intro V1-AhZN3Bv_OCg.mp4 7.42 MB
Part 01-Module 04-Lesson 01_Factors/03. M4 L1A 03 Example Of A Factor V4-MJrwJDjWlAg.mp4 7.41 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/01. M2L6 01 Intro V3-CQ6QGAxbUF8.mp4 7.39 MB
Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 15 V1 (1)-Q5CZxxKXAB8.mp4 7.39 MB
Part 06-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.mp4 7.38 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 53 Additive Feature Att Part 1 V2-uTpfEfHp_KA.mp4 7.37 MB
Part 03-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.mp4 7.36 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.mp4 7.33 MB
Part 01-Module 02-Lesson 01_Quant Workflow/02. M2L1 01 Starting From A Hypothesis V3-yjlt4yerB9I.mp4 7.32 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/06. M4 L2b 06 The Core Idea V3-0KwLkaKHAvg.mp4 7.32 MB
Part 07-Module 01-Lesson 03_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.31 MB
Part 01-Module 02-Lesson 05_Volatility/02. M2L5 02 Historical Volatility V3-BOPhsYLHkUU.mp4 7.3 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/08. M2L2 07 Handling Outliers In Signal Returns V4-ILdnNi4CgZM.mp4 7.3 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.mp4 7.29 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/19. What Is A P-value Anyway-eU6pUZjqviA.mp4 7.27 MB
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.25 MB
Part 07-Module 01-Lesson 02_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.25 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.mp4 7.25 MB
Part 01-Module 04-Lesson 06_Alpha Factors/19. M4 L3a 10 Factor Returns V5-enyeTpyCS-o.mp4 7.22 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/10. L3 08 The Efficient Frontier V3-tEEyhU23bI4.mp4 7.22 MB
Part 01-Module 04-Lesson 06_Alpha Factors/07. M4 L3a 052 Controlling For Risk Within An Alpha Factor Part 2 V2-Ks8HiHcflPs.mp4 7.21 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.21 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.21 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 21 Optional Explanation Part 1 V2-7shDUICrpro.mp4 7.15 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.mp4 7.14 MB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/02. Jupyter-qiYDWFLyXvg.mp4 7.12 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/03. 3 Word2Vec Notebook V2-4cWzv3YiF_w.mp4 7.12 MB
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.mp4 7.08 MB
Part 01-Module 01-Lesson 07_Stock Returns/05. M1L5 06 Distribution Of Stock Prices Part 2 V1-cGoXGiO1DYk.mp4 7.07 MB
Part 01-Module 01-Lesson 05_Market Mechanics/03. M1L3 03 Trading Stocks V3-GHoRtfUrUMc.mp4 7.04 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/21. M4 L3b 17 IVol Idiosyncratic Volatility V2-B8hOR4G9CJk.mp4 7.04 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 07 Sklearn Code Intro Part 3 V1-e6SZrnZxaTI.mp4 7.02 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 36 Algorithm Part 2 V1-g1nevC4NU2U.mp4 6.99 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.mp4 6.98 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/01. M4 L2b 01 PCA Statistical Risk Model V1-lDxqJ0JYUzs.mp4 6.97 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/01. L3 01 Intro V1-PxLJniuGyC0.mp4 6.94 MB
Part 05-Module 01-Lesson 03_Pandas/08. Pandas 4 V1-eMHUn9v9dds.mp4 6.93 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/06. M4 L1B 05 Covariance Matrix Using Factor Model V3-_qfTLXoifsM.mp4 6.93 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 6.92 MB
Part 07-Module 01-Lesson 03_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 6.92 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 22 CaseA Part 1 V2-2X9531oXeJY.mp4 6.9 MB
Part 02-Module 01-Lesson 01_Welcome To Term II/02. AITND TII 02 Overview Of Term 2 V1-dVz-lVGvadY.mp4 6.89 MB
Part 01-Module 01-Lesson 06_Data Processing/04. M1L4 06 Technical Indicators V6-jo740Kq3YN4.mp4 6.89 MB
Part 02-Module 01-Lesson 05_Financial Statements/06. M5 SC 1 Raw Strings V1-WhL1VbulThY.mp4 6.88 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/21. PyTorch V2 Part 7 Solution V1-d_NhvI1yEf0.mp4 6.8 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.mp4 6.78 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/02. 02. What Is Vision-_99V1rUNFa4.mp4 6.75 MB
Part 01-Module 02-Lesson 05_Volatility/03. M2L5 03 Annualized Volatility V8-yakh1pjP7uY.mp4 6.74 MB
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.mp4 6.72 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 37 Attributes V1-IMhB5bOK7Wg.mp4 6.71 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/02. M4 L4 02 Setting Up The Problem Alphas V5-6GeyU-thC4U.mp4 6.71 MB
Part 03-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.mp4 6.71 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.mp4 6.71 MB
Part 01-Module 01-Lesson 04_Stock Prices/02. M1L2 02 Stock Prices V7-l_PilXVuh8I.mp4 6.68 MB
Part 02-Module 03-Lesson 04_Random Forests/06. L4 011 HS Random Forests V5-TSpYXdBYo1s.mp4 6.65 MB
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.mp4 6.61 MB
Part 05-Module 01-Lesson 02_NumPy/11. NumPy 6 V1-wtLRuGK0kW4.mp4 6.61 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.61 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.61 MB
Part 02-Module 03-Lesson 05_Feature Engineering/03. M7L5 03 Setup Code Exercise V1-xlwlc6l7pXg.mp4 6.59 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.mp4 6.58 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/06. PyTorch V2 Part 1 Solution 2 V1-QLaGMz8Ca3E.mp4 6.57 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/13. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.55 MB
Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Part 1 V2-H5hJOG1DjBU.mp4 6.54 MB
Part 06-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.mp4 6.54 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.mp4 6.54 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4 6.52 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4 6.52 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/25. L1 29 Open End Funds Holding Cash For Withdrawals V3-RU8-ZRBJ2Cw.mp4 6.5 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/20. M4 L1B 19 Index Changes V1-C7QNfPZBXXo.mp4 6.47 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/05. 4 EncodingWords Sol V1-4RYyn3zv1Hg.mp4 6.45 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4 6.42 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/15. M4 L3b 11 Skewness And Momentum Defining Skew V2-6PgqIpmJBJ8.mp4 6.4 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/26. L1 30 ClosedEnd Mutual Funds V3-y2VhtrF6vdc.mp4 6.39 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/07. 6 Cleaning And Padding V1-UgPo1_cq-0g.mp4 6.36 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/feb-26-2019-16-19-56.gif 6.33 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.mp4 6.33 MB
Part 02-Module 03-Lesson 02_Decision Trees/05. Recommending Apps-nEvW8B1HNq4.mp4 6.32 MB
Part 07-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.32 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/09. M2L6 13 Trade Pairs Of Stocks V6-i1yVMrgjtB0.mp4 6.32 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.mp4 6.32 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/17. New Mean and Variance Solution - Artificial Intelligence for Robotics-SwxRWZaC1FM.mp4 6.31 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.mp4 6.3 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/02. L1 01 Stocks V2-XHo5iyxDxOQ.mp4 6.3 MB
Part 04-Module 01-Lesson 02_Vectors/03. Vectors 3-mWV_MpEjz9c.mp4 6.29 MB
Part 02-Module 03-Lesson 07_Feature Importance/02. L7 H3 V1-xaXHTDANbnc.mp4 6.29 MB
Part 06-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.mp4 6.27 MB
Part 01-Module 01-Lesson 08_Momentum Trading/09. M1L6 09 Statistical Analysis V10-_p1m_q8jE6E.mp4 6.26 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.mp4 6.26 MB
Part 01-Module 02-Lesson 03_Regression/04. M2L3 04 Parameters Of A Distribution V3--akdmiLDny4.mp4 6.25 MB
Part 02-Module 03-Lesson 05_Feature Engineering/01. M7L5 Intro V1-lSJ3XajG1L8.mp4 6.24 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/05. M4 L4 06 Standard Constraints V4-OPBKsNQPr6I.mp4 6.22 MB
Part 01-Module 04-Lesson 06_Alpha Factors/03. M4 L3a 02 Alpha Factors Versus Risk Factor Modeling V2-qsahBvhVTkk.mp4 6.18 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.17 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4 6.17 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 62 Shap V1-KidZpvQ9Sus.mp4 6.14 MB
Part 01-Module 02-Lesson 03_Regression/02. M2L3 02 Distributions V2-ZlRGxq5I9BU.mp4 6.13 MB
Part 07-Module 01-Lesson 02_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.09 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.mp4 6.08 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/09. 7 Batching Data Solution V1-nu2rjLzt1HI.mp4 6.06 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.mp4 6.06 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/11. MV 05 Time Management V1-22PdQNlhCt8.mp4 6.03 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/15. M4 L2A 10 Portfolio Variance Using Factor Model V4-V06aCZUvgbo.mp4 6.01 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/19. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.01 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/01. AIT M5L5 01 Introduction V2-94CP-oy5KKI.mp4 6 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/02. M4L52 HSA Embedding Weight Matrix V3 RENDER V2-KVCcG5v8fi0.mp4 5.99 MB
Part 02-Module 01-Lesson 05_Financial Statements/01. AIT M5L4A 01 Intro V1-BS4n9rRYGtw.mp4 5.98 MB
Part 01-Module 01-Lesson 07_Stock Returns/03. M1L5 03 Log Returns V5-62fZN1QnGjc.mp4 5.93 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 26 CaseC Solution V1-jcAiG0nLByI.mp4 5.93 MB
Part 01-Module 04-Lesson 01_Factors/05. M4 L1A 04 Standardizing A Factor V5-sLZY2SQ4uME.mp4 5.93 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.mp4 5.92 MB
Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Part II-1GRv1S6K8gQ.mp4 5.92 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/07. PyTorch V2 Part 1 Solution 3 V1-iMIo9p5iSbE.mp4 5.9 MB
Part 03-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.mp4 5.9 MB
Part 01-Module 02-Lesson 03_Regression/15. M2L3 14 Regression In Trading V2-bcOGRWxg7qQ.mp4 5.86 MB
Part 02-Module 03-Lesson 04_Random Forests/08. L4 15 HS Outofbag Score V4-CcdXrGYaOhE.mp4 5.86 MB
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.mp4 5.84 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 23 CaseB Intro V1-8RKZrweh0Mw.mp4 5.84 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/10. M4 L3b 08 Winners And Losers Approximating Curves With Polynomials V4-Nw6v2EeECt0.mp4 5.83 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/18. 05 Batching Data V1-9Eg0wf3eW-k.mp4 5.82 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/27. L1 31 Transaction Costs V2-JGYAv7tQpyY.mp4 5.79 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 4 V1-Eg9DvNMi1v8.mp4 5.77 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/12. L1 13 Hang Seng Index Construction V2-rdGdC-meRLU.mp4 5.77 MB
Part 01-Module 01-Lesson 08_Momentum Trading/02. M1L6 02 Momentumbased Signals V4-RedwbmYg6e4.mp4 5.75 MB
Part 01-Module 04-Lesson 01_Factors/09. M4 L1A 08 Rescale Part 2 V3-8Ix10U6MEug.mp4 5.75 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 5.75 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 5.75 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.mp4 5.74 MB
Part 02-Module 03-Lesson 05_Feature Engineering/11. M7L5 Outro V1-k7DsfRhDwLQ.mp4 5.73 MB
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.mp4 5.7 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/17. M4 L1B 16 Fundamentals V1-rPii5-ry8nc.mp4 5.7 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/23. M4 L3b 19 IVol Quantamental Investing V2-K6Ud6gams-U.mp4 5.7 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 5.69 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 5.69 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/27. MV When Those Around You Dont Believe In You V1--vKspTOIXY0.mp4 5.69 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/22. L2 09 Lists And Membership Operators V2-rNV_E50wcWM.mp4 5.67 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/08. 6 Defining Context Targets V1-DJN9MzD7ctY.mp4 5.67 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/02. L3 02 Diversification V3-tyzqlXddXd8.mp4 5.67 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/20. Notation for Random Variables-8NxTW1u4s-Y.mp4 5.66 MB
Part 03-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.mp4 5.64 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/03. Testing-gmxGRJSKEb0.mp4 5.63 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/10. 9 DefiningModel V1-SpvIZl1YQRI.mp4 5.63 MB
Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Part 2 V1-1WK5VAMzQ2Q.mp4 5.63 MB
Part 01-Module 03-Lesson 02_ETFs/02. L2 12 Shortcomings Of Mutual Funds V2-oEqsaex31Qg.mp4 5.62 MB
Part 01-Module 02-Lesson 05_Volatility/10. M2L5 09 Forecasting Volatility V3-82v4v_PKDAE.mp4 5.6 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/06. M4 L3b 05 Overnight Returns Methods Quantile Analysis V3-4Js3mghq2mU.mp4 5.6 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/24. L1 27 OpenEnd Mutual Funds V2-T4_mmjEKUAo.mp4 5.6 MB
Part 03-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.mp4 5.58 MB
Part 01-Module 02-Lesson 03_Regression/18. MV 14 What Happens In Your Brain V1-ioDP7ndd40Y.mp4 5.58 MB
Part 02-Module 01-Lesson 04_Feature Extraction/09. T-SNE-xxcK8oZ6_WE.mp4 5.56 MB
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.mp4 5.55 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/01. M4 L3b 01 Case Studies Intro V3-oWWrWbzDi2k.mp4 5.55 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/03. M4 L3b 02 Overnight Returns Abstract V2-q5xidwa5W8w.mp4 5.55 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 41 Wrapper Solution V1-27tEa_Bpq20.mp4 5.53 MB
Part 01-Module 04-Lesson 01_Factors/06. M4 L1A 05 Demean Part 1 V3-R3N8bd8U6TM.mp4 5.52 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 49 Subsets Solution V1-YfwUDZ_hNKI.mp4 5.52 MB
Part 01-Module 04-Lesson 06_Alpha Factors/38. M4 L3a 171 Turnover As Proxy For Real World Constraints V2-6xo8sZjoSVk.mp4 5.5 MB
Part 06-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.mp4 5.49 MB
Part 01-Module 03-Lesson 02_ETFs/01. L2 01 Intro V2-utlPzT8MEsM.mp4 5.48 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/08. 7 PaddedFeatures Sol V1-sYOd1IDmep8.mp4 5.48 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/06. M2L6 09 Cointegration V6-N4ZI5SyFMOc.mp4 5.47 MB
Part 01-Module 04-Lesson 06_Alpha Factors/45. M4 L3a 19 Quantiles Academic Research Vs Practitioners V2-AwL7cV2VyhM.mp4 5.47 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.mp4 5.46 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/16. M4 L4 20 Outro V1-c3J8t6q2BGo.mp4 5.45 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/06. L1 07 Ratios V2-Dfbwep-tkok.mp4 5.43 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/08. M4 L2b 08 Writing It Down Pt 1 V3-NyDNFqm8c_s.mp4 5.43 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/07. M4 L4 08 Factor Exposure And Position Constraints V3-wMY4zI5zLSM.mp4 5.42 MB
Part 02-Module 03-Lesson 02_Decision Trees/08. Student Admissions-TdgBi6LtOB8.mp4 5.41 MB
Part 07-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.41 MB
Part 07-Module 01-Lesson 02_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.41 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/03. L1 02 Indices V2-BRv5B78YBGs.mp4 5.41 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/14. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.41 MB
Part 01-Module 04-Lesson 06_Alpha Factors/23. M4 L3a 11 Universe Construction Rule V3-Cr0-k7gUSNg.mp4 5.4 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/04. L3 04 Portfolio Variance V2-LlxRypakop4.mp4 5.39 MB
Part 01-Module 04-Lesson 09_Project 4 Alpha Research and Factor Modeling/01. M4 01 Intro To Project 4 V1-7goOG7CdUjU.mp4 5.39 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 22 CaseA Part 3 V1-EM21p54qrp0.mp4 5.39 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.mp4 5.39 MB
Part 01-Module 02-Lesson 01_Quant Workflow/04. M2L1 03 Flavors Of Trading Strategies V4-uCCx8I9u_Nk.mp4 5.38 MB
Part 06-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.mp4 5.36 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/02. Structured Languages-NsmqUIHlk6U.mp4 5.36 MB
Part 10-Module 01-Lesson 01_Intro to NLP/03. Structured Languages-NsmqUIHlk6U.mp4 5.36 MB
Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 16 Sector Intro V2-NSp_qX8s_B0.mp4 5.35 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.35 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.35 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.33 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.33 MB
Part 03-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules-jPGyFgcIvsM.mp4 5.33 MB
Part 02-Module 03-Lesson 06_Overlapping Labels/08. L6 11 HS Foreshadow V2-iXqYUwpFTqs.mp4 5.31 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.mp4 5.31 MB
Part 02-Module 03-Lesson 07_Feature Importance/03. L7 H4 V1-CHo9WLLAOl0.mp4 5.31 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/04. M4 L2b 04 Bases As Languages V3-yEL0-AE3mjo.mp4 5.3 MB
Part 02-Module 03-Lesson 01_Overview/01. L1 01 HS Welcome To M7 V2-3l--nlBdfaA.mp4 5.28 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.mp4 5.28 MB
Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Solution V1-FBOlA2NaK3k.mp4 5.27 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 21 Optional Explanation Part 3 V1-xkhSC032Fxs.mp4 5.26 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/11. L1 12 How An Index Is Constructed V2-dsbi4dvdU9c.mp4 5.26 MB
Part 06-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.mp4 5.26 MB
Part 03-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.mp4 5.25 MB
Part 01-Module 01-Lesson 07_Stock Returns/01. M1L5 02 Returns V6-PngIo6G73Z8.mp4 5.24 MB
Part 06-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.mp4 5.21 MB
Part 03-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.mp4 5.21 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.mp4 5.2 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/01. M4L51 HSA Word Embeddings V3 RENDER V1-ZsLhh1mly9k.mp4 5.18 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/04. M4 L3b 03 Overnight Returns Possible Alpha Factors V2-QBCDr9q2rLo.mp4 5.18 MB
Part 07-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.17 MB
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.mp4 5.17 MB
Part 01-Module 01-Lesson 06_Data Processing/03. M1L4 04 Corporate Actions V5-S60WArbQK7k.mp4 5.14 MB
Part 07-Module 01-Lesson 02_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.14 MB
Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.13 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.13 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.13 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.mp4 5.11 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 35 Tree Shap Intro V1-Si3r4-VR0CU.mp4 5.1 MB
Part 05-Module 01-Lesson 02_NumPy/09. NumPy 5 V1-vGjI-WTnEbY.mp4 5.09 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.mp4 5.08 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/14. L1 16 Funds V2-s9f2Bzc9lnk.mp4 5.06 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/04. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.04 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/06. M8l2 06 Scaling Alpha V2--gXKutS0jQc.mp4 5.04 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.mp4 5.02 MB
Part 03-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.mp4 5 MB
Part 01-Module 02-Lesson 01_Quant Workflow/05. M2L1 04 Anatomy Of A Strategy Part 2 V1-v3w4JZKQixc.mp4 5 MB
Part 01-Module 02-Lesson 01_Quant Workflow/01. MV 05 Intro To Module 2 V1-92JzOXda9Q8.mp4 4.97 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/01. Welcome to NLP-g-AlFF61p0I.mp4 4.97 MB
Part 10-Module 01-Lesson 01_Intro to NLP/02. Welcome to NLP-g-AlFF61p0I.mp4 4.97 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/12. M4 L4 14 Transaction Costs V3-yxwqTvbJhhc.mp4 4.97 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/02. L4 02 What Is Optimization V2-ISRlP1GeOjU.mp4 4.97 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/15. M82 Outro V1-GbWg0wSTDfk.mp4 4.96 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/15. L1 18 Alpha And Beta V3-CcVdfrr5nD8.mp4 4.93 MB
Part 02-Module 01-Lesson 03_Text Processing/10. Stemming And Lemmatization-7Gjf81u5hmw.mp4 4.93 MB
Part 01-Module 04-Lesson 06_Alpha Factors/37. M4 L3a 162 Real World Constraints Transaction Costs V2-HAif7xSh8z0.mp4 4.91 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.mp4 4.9 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 38 Proportion Intro V1-gMkMy0BeuaU.mp4 4.9 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/05. Model Complexity Graph-NnS0FJyVcDQ.mp4 4.9 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.mp4 4.89 MB
Part 02-Module 03-Lesson 06_Overlapping Labels/01. L6 01 HS Intro V2-zUOOluyHe_E.mp4 4.89 MB
Part 04-Module 01-Lesson 02_Vectors/01. Vectors 1-oPBz-MLVUHk.mp4 4.88 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/21. L1 24 Hedging Strategies V3-8bzw4ZMGpWU.mp4 4.86 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/01. M82 Intro V1-WQ95rYDyVSA.mp4 4.85 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/23. Error Function-V5kkHldUlVU.mp4 4.84 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/23. Error Function-V5kkHldUlVU.mp4 4.84 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/22. L2 07 Lists And Membership Operators II V3-3Nj-b-ZzqH8.mp4 4.83 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 21 Optional Explanation Part 2 V1-zpn4NQNQJh8.mp4 4.82 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.mp4 4.82 MB
Part 01-Module 02-Lesson 03_Regression/10. M2L3 09 Transforming Data V3-N8Fhq8wiQZU.mp4 4.82 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 66 Discussion V1-oTUWNw8X45M.mp4 4.81 MB
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.mp4 4.81 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.mp4 4.8 MB
Part 02-Module 03-Lesson 02_Decision Trees/03. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 4.8 MB
Part 07-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 4.8 MB
Part 01-Module 02-Lesson 05_Volatility/06. M2L5 06 Rolling Windows V3-4EuMKqeNXA0.mp4 4.79 MB
Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 10 Dispersion Solution V1-S5lzU1nAuhU.mp4 4.77 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/03. KALMAN Tracking Intro RENDER V2-C73G7vfVNQc.mp4 4.74 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/28. L1 32 Summary V1-Pt2sVftdwS0.mp4 4.74 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.mp4 4.73 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.mp4 4.73 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/09. M4 L3b 07 Winners And Losers Accelerated And Decelerated Gains And Losses V2-cdSdKl4uxVM.mp4 4.71 MB
Part 02-Module 01-Lesson 04_Feature Extraction/08. Embeddings For Deep Learning-gj8u1KG0H2w.mp4 4.7 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 09 Gini Solution V1-xCjkhgQDTu4.mp4 4.69 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 65 Rank Shap Solution V1-jkAxXatozUo.mp4 4.68 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.mp4 4.67 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/15. 11 SkipGram Negative V1-e7ZrzpyXNDs.mp4 4.67 MB
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.67 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.mp4 4.66 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.mp4 4.66 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.mp4 4.66 MB
Part 01-Module 03-Lesson 02_ETFs/05. L2 07 ETF Sponsor V2-v5vfAP1nJ10.mp4 4.66 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/19. M4 L1B 18 EventDriven Factors V1-2mnwjChH2hg.mp4 4.64 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/04. Unstructured Text-OmwSdaec5vU.mp4 4.63 MB
Part 10-Module 01-Lesson 01_Intro to NLP/05. Unstructured Text-OmwSdaec5vU.mp4 4.63 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/06. Context-J-4pfu2w1C0.mp4 4.62 MB
Part 10-Module 01-Lesson 01_Intro to NLP/07. Context-J-4pfu2w1C0.mp4 4.62 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 22 Relative Returns V2-m4MvYRlyPoU.mp4 4.62 MB
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.mp4 4.62 MB
Part 02-Module 03-Lesson 07_Feature Importance/01. L7 Intro V1-tNGSsp6vUvY.mp4 4.62 MB
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.mp4 4.61 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/03. 03. Role In AI Render-xm1TXnNe5Pw.mp4 4.6 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/09. M8l2 18 Linear Price Impact Part2 V2-aTNfxMjEg3w.mp4 4.6 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/18. M4 L3b 14 IVol Value And Idiosyncratic Volatility Overview V2-h7vamh2FPMs.mp4 4.6 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/06. 5 GettingRid ZeroLength V1-Hs6ithuvDJg.mp4 4.59 MB
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.59 MB
Part 01-Module 04-Lesson 06_Alpha Factors/27. M4 L3a 13 Sharpe Ratio V4-W8nfg1fkloA.mp4 4.58 MB
Part 01-Module 01-Lesson 08_Momentum Trading/13. M1L6 12 Finding Alpha V1-r8lfWVhfQC0.mp4 4.53 MB
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.mp4 4.52 MB
Part 01-Module 04-Lesson 01_Factors/08. M4 L1A 07 Rescale Part 1 V2-BcsxA0vy3jA.mp4 4.52 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/16. M4 L2A 27 Summary V1-rdqINNkTlqs.mp4 4.52 MB
Part 02-Module 03-Lesson 04_Random Forests/12. L4 17 HS Outro V2-oH7B6EyLE0k.mp4 4.52 MB
Part 01-Module 01-Lesson 06_Data Processing/08. M1L4 11 Survivor Bias V2-39MeCCw5ndM.mp4 4.52 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.mp4 4.51 MB
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.mp4 4.5 MB
Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 16 Sector Intro Part 2 V1-DmYfsKS6kRM.mp4 4.49 MB
Part 03-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.mp4 4.48 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.mp4 4.47 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.mp4 4.45 MB
Part 01-Module 01-Lesson 08_Momentum Trading/06. M1L6 06 Trading Strategy V2-rrCHC20FkIc.mp4 4.43 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/02. M2L4 02 Autoregressive Models V5-9jE7S4b-oIU.mp4 4.42 MB
Part 06-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.mp4 4.4 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 36 Algorithm Part 1 V2-4ko5-Ck-yCQ.mp4 4.39 MB
Part 01-Module 03-Lesson 05_Project 3 Smart Beta and Portfolio Optimization/01. MV 12 Transition To Project 03 V1-ClzlNlWqMQI.mp4 4.38 MB
Part 01-Module 03-Lesson 02_ETFs/03. L2 03 Commodity Futures V3-qvSubjxMGJ0.mp4 4.37 MB
Part 01-Module 01-Lesson 05_Market Mechanics/08. M1L3 10 Volume V3-DFp7kp0xRCo.mp4 4.37 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.mp4 4.36 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.mp4 4.36 MB
Part 05-Module 01-Lesson 02_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.mp4 4.35 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/07. L1 08 SP Index Categories V2-D3VGIvti71g.mp4 4.34 MB
Part 01-Module 04-Lesson 09_Project 4 Alpha Research and Factor Modeling/04. M4 03 Coming In Term II V1-2jF5J8MIdqc.mp4 4.33 MB
Part 01-Module 04-Lesson 06_Alpha Factors/41. M4 L3a 181 Quantile Analysis Part 1 V2-oT5GFbg0G8g.mp4 4.33 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/04. M4 L4 04 Regularization V4-fq-CanyDHuw.mp4 4.31 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/17. L1 19 Smart Beta V2-Rc9NEmNMzk8.mp4 4.3 MB
Part 02-Module 03-Lesson 02_Decision Trees/10. Entropy Formula-iZiSYrOKvpo.mp4 4.3 MB
Part 07-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.3 MB
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.mp4 4.26 MB
Part 07-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4 4.25 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/14. M2L6 20 Summary V2-wuzha8SU2jw.mp4 4.25 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/05. M4 L2A 16 Fama French Size Factor V2-94a2ugitC_E.mp4 4.24 MB
Part 03-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.mp4 4.24 MB
Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.mp4 4.24 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.mp4 4.23 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 29 Shapley Intro V1-yOfZDm99Vac.mp4 4.23 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.22 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.22 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/05. M2L2 04 Spotting Outliers In Raw Data V3-kFIB0YIW1TQ.mp4 4.22 MB
Part 01-Module 04-Lesson 06_Alpha Factors/48. M4 L3a 21 Its All Relative V2-VBcOrT7TuFA.mp4 4.22 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.mp4 4.22 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/13. M4 L2A 24 Categorical Variable Estimation V4-50hvTluqz3U.mp4 4.22 MB
Part 06-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.mp4 4.21 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.mp4 4.21 MB
Part 01-Module 01-Lesson 08_Momentum Trading/01. M1L6 01 Designing A Trading Strategy V4-O7c6bPXBUsU.mp4 4.2 MB
Part 02-Module 03-Lesson 02_Decision Trees/04. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.19 MB
Part 07-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.19 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.mp4 4.18 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.mp4 4.18 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/16. M4 L2b 15 PCA As A Factor Model Pt 1 V3-4E3C5E-MmkI.mp4 4.17 MB
Part 02-Module 03-Lesson 02_Decision Trees/07. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.16 MB
Part 07-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.16 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.mp4 4.16 MB
Part 01-Module 03-Lesson 02_ETFs/08. L2 10 Lower Operational Costs And Taxes V2-UlJusglK0h0.mp4 4.16 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/01. 矩阵介绍-Ugx3mldc0lE.mp4 4.15 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/09. L1 10 Market Cap Weighting V2-7qVVA5yLFnY.mp4 4.15 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/05. AIT M5L5 06 World Lists Searches V2-RutjcGh74Mw.mp4 4.15 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/10. L1 11 Adding Or Removing From An Index V2-_bWIZWa20j8.mp4 4.15 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.14 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.14 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 08 Gini Intro V1-_Ar4nlfUUEM.mp4 4.13 MB
Part 07-Module 01-Lesson 03_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4 4.11 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/26. Why Are Sampling Distributions Important-aDFDOCJKoH0.mp4 4.09 MB
Part 01-Module 01-Lesson 06_Data Processing/15. MV 06 Our Goal Is To Help You Meet Your Goals V1--pSppDzJRu8.mp4 4.08 MB
Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 09 Dispersion Intro Part 1 V2-xCGuuymx180.mp4 4.07 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.mp4 4.06 MB
Part 03-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.mp4 4.03 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/03. LSTM Basics-gjb68a4XsqE.mp4 4.03 MB
Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 13 Marketvol Intro V1-G03W42Z5RSo.mp4 4.02 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.01 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.01 MB
Part 02-Module 01-Lesson 04_Feature Extraction/02. Bag Of Words-A7M1z8yLl0w.mp4 4.01 MB
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.mp4 4.01 MB
Part 01-Module 03-Lesson 02_ETFs/03. L2 02 Commodities V2-gc_GMqbCC2Q.mp4 4.01 MB
Part 02-Module 01-Lesson 05_Financial Statements/08. M5 SC 3 Finding Metacharacters V1-RiSVD9E823Q.mp4 4 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.mp4 3.99 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.mp4 3.98 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/17. M4 L2A 11 Types Of Risk Models V1-SHj2VzJggAE.mp4 3.98 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/02. M2L6 02 Mean Reversion V5-zQ08lFcZa_A.mp4 3.97 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/12. M4 L2A 08 Variance Of 2 Stocks Part 1 V3-PlPusmuR20k.mp4 3.96 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/27. L2 04 Tuples V3-33xN-AbTMoc.mp4 3.96 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 3.95 MB
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.mp4 3.94 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/03. L1 03 Indices Are Virtual Portfolios V2-oAd_szbBNWc.mp4 3.94 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/02. M4 L2b 02 Vector Two Ways V3-mlw6FnCUloU.mp4 3.94 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.mp4 3.94 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/09. M4 L2A 20 Fama French Risk Model V3-tepvGkpNKrI.mp4 3.93 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.mp4 3.92 MB
Part 01-Module 04-Lesson 01_Factors/07. M4 L1A 06 Demean Part 2 V2-aaj1QVsSCIs.mp4 3.9 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.mp4 3.9 MB
Part 07-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 3.9 MB
Part 02-Module 03-Lesson 07_Feature Importance/14. L7 Outro V1-Y2E-XN3lnWM.mp4 3.89 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.mp4 3.89 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.mp4 3.87 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/15. L1 17 Active Vs Passive V2-QzoHmUzJ5zw.mp4 3.86 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/01. M8L1 01 Intro V2-RjUIPy_robk.mp4 3.86 MB
Part 07-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.mp4 3.85 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 3.85 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 3.85 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.mp4 3.85 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 3.85 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 3.85 MB
Part 06-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.mp4 3.84 MB
Part 01-Module 01-Lesson 08_Momentum Trading/14. MV 13 Global Talent Is Equally Distributed V1-QwDJbbBl_48.mp4 3.84 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/01. M1L1 01 Welcome V1-W2R32yXgwcg.mp4 3.84 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 47 Marginal Solution V1-HINVjMBV6O8.mp4 3.83 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/05. M8l2 05 Holdings Dollars V1-GKA0cpKx0BE.mp4 3.82 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.mp4 3.81 MB
Part 02-Module 01-Lesson 04_Feature Extraction/07. GloVe-KK3PMIiIn8o.mp4 3.81 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.mp4 3.81 MB
Part 05-Module 01-Lesson 03_Pandas/04. Pandas 1 V1-iXnYN8cnhzs.mp4 3.8 MB
Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.mp4 3.79 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 64 Rank Shap Intro V1-7b_CpMxqVYc.mp4 3.78 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/02. L1 00 Intro V2-JA4WBd6sHF4.mp4 3.78 MB
Part 01-Module 04-Lesson 06_Alpha Factors/10. M4 L3a 07 Ranking Part 2 V2-uwPUV5LBhWY.mp4 3.77 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.mp4 3.77 MB
Part 05-Module 01-Lesson 03_Pandas/05. Pandas 2 V1-B7MuFIwboKU.mp4 3.77 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 28 CaseD Solution V1-gRSj2_SBwnM.mp4 3.77 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/03. M2L4 03 Moving Average Models V5-1FkCP_dwxjI.mp4 3.76 MB
Part 01-Module 01-Lesson 04_Stock Prices/02. M1L2 01 Stock Pt II V1-SGb54HLbk1g.mp4 3.76 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.76 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.76 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.mp4 3.75 MB
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.mp4 3.75 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.mp4 3.74 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/10. M8l2 12 Optimization Without Constraints Part2 V2-fvwhoqt9U70.mp4 3.73 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/17. M4 L2b 16 PCA As A Factor Model Pt 2 V3-lcd-muqX5og.mp4 3.72 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.mp4 3.72 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/13. L1 15 Calculating Index After Add Or Delete V2-hiAHRE6JY0k.mp4 3.7 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.mp4 3.7 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/04. L1 05 Market Cap V2-PE0UgUc0f0U.mp4 3.69 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/26. M4 L3b 22 Summary V2-Tq8yVPEHxXs.mp4 3.67 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.mp4 3.67 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.66 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.66 MB
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.mp4 3.66 MB
Part 02-Module 01-Lesson 05_Financial Statements/19. M5 SC 10 Parsing An HTML File V1-Ybl4fI92cYE.mp4 3.65 MB
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.mp4 3.65 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/20. Calculating the p-value-_W3Jg7jQ8jI.mp4 3.64 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/12. M4 L1B 11 How An Alpha Factor Becomes A Risk Factor Part 1 V3-p0cTudt8kXI.mp4 3.64 MB
Part 01-Module 01-Lesson 06_Data Processing/03. M1L4 04b Dividends V2-OVZw9tci55w.mp4 3.62 MB
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.mp4 3.62 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/06. M4 L2A 17 Fama French Size Factor V3-FXZuHsn0bx4.mp4 3.62 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 59 Run Starter Code V1-T3K68vodL5E.mp4 3.6 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/11. Emotion as a Service-2jAP3rP3USM.mp4 3.6 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/04. M8l2 04 Time Offset Take2 V1-TYYV3MnhCP0.mp4 3.6 MB
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.mp4 3.59 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/02. RNN Vs LSTM-70MgF-IwAr8.mp4 3.58 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/14. M4 L1B 13 Momentum Or Reversal V3-izTAHVF6V_g.mp4 3.57 MB
Part 01-Module 03-Lesson 02_ETFs/04. L2 06 Hedging V3-4k1bdohhawI.mp4 3.57 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.mp4 3.57 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/03. L1 04 Indices Describe The Market V2-jNzwxE3el7I.mp4 3.57 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/13. PyTorch V2 Part 3 Solution 2 V1-ExyFG2MjsKs.mp4 3.56 MB
Part 01-Module 03-Lesson 02_ETFs/10. L2 12 Misaligned ETF Pricing V3-5-pBZ3fyv6I.mp4 3.55 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.54 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.54 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.mp4 3.54 MB
Part 01-Module 01-Lesson 05_Market Mechanics/01. M1L3 01 Intro V4-LE-4Xf8lzHk.mp4 3.53 MB
Part 01-Module 03-Lesson 02_ETFs/09. L2 11 Arbitrage V2-yp-CcGrMzYQ.mp4 3.53 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/01. M2L4 01 Time Series Modeling V4-QeIu7GMZl20.mp4 3.52 MB
Part 05-Module 01-Lesson 03_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.mp4 3.51 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.mp4 3.51 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/01. M4 L2A 12 Time Series Risk Model Factor Variance V2-hjVBXeZmA0w.mp4 3.5 MB
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.mp4 3.5 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/01. M4 L1B 01 Intro To Lesson V1-ff0paDNA75U.mp4 3.49 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/10. M4 L2A 06 Variance Of One Stock V3-rxaABg4wVZo.mp4 3.49 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 54 Test1 V1-6yMdxyiykeg.mp4 3.48 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/10. M4 L2A 21 Cross Sectional Risk Model V3-mpnRAt8qUus.mp4 3.48 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/05. M4 L2A 03 Factor Model Of Asset Return V2-7UnllxDmLj8.mp4 3.48 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/07. M4 L2A 04 Factor Model Of Portfolio Return V3-HEoPljS1wD0.mp4 3.47 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/10. Feature Extraction-UgENzCmfFWE.mp4 3.47 MB
Part 10-Module 01-Lesson 01_Intro to NLP/11. Feature Extraction-UgENzCmfFWE.mp4 3.47 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.46 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/15. M4 L2A 26 Fundamental Factors V2-fndhL2Tolac.mp4 3.45 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/27. M4 L1B 26 Summary V1-yuLQA24Thms.mp4 3.44 MB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/01. M4 L4 01 Intro V1-9NzZFszX2E4.mp4 3.44 MB
Part 10-Module 01-Lesson 01_Intro to NLP/01. Intro Arpan-MW5MWOLj064.mp4 3.44 MB
Part 02-Module 03-Lesson 06_Overlapping Labels/05. L6 06 HS Adjust Bag Size V4-BZbgljJnGYE.mp4 3.42 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 39 Proportion Solution V1-i03PYAy2ijE.mp4 3.42 MB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/07. 5 Subsampling Solution V1-YXruURuFD7g.mp4 3.42 MB
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.mp4 3.42 MB
Part 06-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.mp4 3.41 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 55 Test2 V1-36-igThq7yI.mp4 3.4 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/07. Natural Language Processing-UQBxJzoCp-I.mp4 3.39 MB
Part 10-Module 01-Lesson 01_Intro to NLP/08. Natural Language Processing-UQBxJzoCp-I.mp4 3.39 MB
Part 03-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.mp4 3.39 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 3 V1-QOwy_XTIAes.mp4 3.38 MB
Part 02-Module 03-Lesson 06_Overlapping Labels/03. L6 03 HS The Problem V4-4nTtrMyP6zQ.mp4 3.38 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/05. L1 06 Growth Vs Value V2-ZCjre5YTD0s.mp4 3.37 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.mp4 3.37 MB
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.mp4 3.36 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.mp4 3.36 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.mp4 3.36 MB
Part 01-Module 02-Lesson 01_Quant Workflow/05. M2L1 04 Anatomy Of A Strategy Part 1 V5-cnJK8c2zfq4.mp4 3.35 MB
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.mp4 3.35 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/08. M8l2 10 Linear Impact V2-ntAIxAOzQQM.mp4 3.34 MB
Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4 3.34 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/14. PyTorch - Part 4-AEJV_RKZ7VU.mp4 3.32 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.mp4 3.32 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.31 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.31 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 01 Intro In The First Five V1-magg5AVJRVA.mp4 3.31 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.mp4 3.3 MB
Part 07-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.28 MB
Part 01-Module 01-Lesson 04_Stock Prices/01. M1L2 01 Stocks V6-23sv5ey0ySs.mp4 3.26 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Solution Part 1 V2-CliBAanDEhk.mp4 3.25 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.mp4 3.24 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.mp4 3.23 MB
Part 07-Module 01-Lesson 02_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.22 MB
Part 02-Module 01-Lesson 03_Text Processing/05. Tokenization-4Ieotbeh4u8.mp4 3.22 MB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/23. M4 L1B 22 Alternative Data V1-p6NxGZnkrdc.mp4 3.22 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.2 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.2 MB
Part 01-Module 03-Lesson 02_ETFs/11. L2 14 Summary V1-E5br2PiH8kY.mp4 3.2 MB
Part 02-Module 05-Lesson 03_Attribution/05. M8l3 06 Performance Attribution V1-kLGjemiiPIs.mp4 3.19 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 46 Marginal Intro V1-RhDVEN3vHc8.mp4 3.19 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/07. M8l2 07 Tcost Part3 V2-ZFAZASS1m54.mp4 3.18 MB
Part 02-Module 03-Lesson 04_Random Forests/04. MLND SL DT 13 Random Forests MAIN V2-4xhjf6s_Pr0.mp4 3.17 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.mp4 3.17 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 32 Discussion Solution V1-pxmaMYOtNys.mp4 3.16 MB
Part 03-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.mp4 3.15 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/19. L1 21 Hedge Funds V4-AgGPqvDFTHw.mp4 3.15 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 48 Subsets Intro V1-ddoL_RcpZAc.mp4 3.15 MB
Part 02-Module 01-Lesson 03_Text Processing/04. Normalization-eOV2UUY8vtM.mp4 3.13 MB
Part 01-Module 04-Lesson 06_Alpha Factors/16. M4 L3a 09 Smoothing V2-mAfrjpZOf7Q.mp4 3.11 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4 3.11 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4 3.11 MB
Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.mp4 3.11 MB
Part 01-Module 01-Lesson 05_Market Mechanics/05. M1L3 08 Tick Data V4-2O0eSKmI6YQ.mp4 3.08 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.mp4 3.07 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/18. KALMAN QUIZ Gaussian Motion 01 RENDER V2-LFPT0R3VaPs.mp4 3.04 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/11. M8l2 13 Risk Factor Matrix V2-Obwjj-Fs2LM.mp4 3.04 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/20. M4 L3b 16 IVol Arbitrage Risk V3-rKtJ3iAYYns.mp4 3.03 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.mp4 3.03 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.03 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.02 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.02 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.mp4 3.01 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/18. L1 20 Mutual Funds V2-LgaylDkS92Y.mp4 3.01 MB
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.mp4 3 MB
Part 02-Module 01-Lesson 04_Feature Extraction/06. Word2Vec-7jjappzGRe0.mp4 2.98 MB
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.mp4 2.98 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.mp4 2.96 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/09. Text Processing-pqheVyctkNQ.mp4 2.96 MB
Part 10-Module 01-Lesson 01_Intro to NLP/10. Text Processing-pqheVyctkNQ.mp4 2.96 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.mp4 2.95 MB
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.mp4 2.92 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/12. M4 L2A 23 Categorical Factors V2-F76juAxHVIk.mp4 2.92 MB
Part 06-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.mp4 2.91 MB
Part 01-Module 04-Lesson 06_Alpha Factors/42. M4 L3a 182 Quantile Analysis Part 2 V3-NF18kx0sfBE.mp4 2.91 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/17. M4 L3b 13 Skewness And Momentum Conditional Factor V2-cMLTVZFKEm0.mp4 2.9 MB
Part 01-Module 03-Lesson 02_ETFs/03. L2 04 Commodity ETFs V2-UpgX6INJ6nU.mp4 2.89 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.mp4 2.88 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/07. L3 06 The Covariance Matrix And Quadratic Forms V1-as5lafBZ2CA.mp4 2.88 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.mp4 2.88 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/14. Character-Wise RNN-dXl3eWCGLdU.mp4 2.88 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.87 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 2.87 MB
Part 01-Module 01-Lesson 08_Momentum Trading/04. M1L6 04 Long And Short Positions V3-TCOFgM-hxkQ.mp4 2.87 MB
Part 02-Module 05-Lesson 03_Attribution/04. M8l3 05 Variance Decomposition V3-2V80v8sQl90.mp4 2.87 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.mp4 2.85 MB
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.mp4 2.85 MB
Part 07-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.mp4 2.84 MB
Part 03-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.mp4 2.84 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 2.83 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 2.83 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/25. M4 L3b 21 IVol Generalizing The Volatility Factor V2-Lt1JPjKHPmk.mp4 2.83 MB
Part 02-Module 01-Lesson 06_Basic NLP Analysis/09. AIT M5L5 99 Summary V1-ThLOv6gDyHI.mp4 2.82 MB
Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4 2.82 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.mp4 2.81 MB
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.mp4 2.81 MB
Part 01-Module 01-Lesson 05_Market Mechanics/09. M1L3 12 Gaps In Market Data V3-jMT3VbUGiZI.mp4 2.8 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/07. 08. Computer Vision Pipeline-64hFcqhnNow.mp4 2.79 MB
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4 2.79 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/07. M2L4 08 Particle Filter V4-4KhDUAvwI74.mp4 2.79 MB
Part 01-Module 01-Lesson 05_Market Mechanics/10. M1L3 14 Markets In Different Timezones V3-wmmEpPM-HVs.mp4 2.78 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.mp4 2.78 MB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/18. PyTorch V2 Part 5 Solution 2 V1-3Py2SbtZLbc.mp4 2.78 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.mp4 2.77 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/09. M4 L2b 09 Writing It Down Pt 2 V2-TSH3hTAHsIg.mp4 2.77 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/07. M4 L2A 18 Fama French Value Factor V4-IcbsQ4QRGbs.mp4 2.76 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/19. M4 L3b 15 IVol Arbitrage And Efficient Pricing Of Stocks V3-7Fqe5DP6iG8.mp4 2.75 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.mp4 2.75 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.mp4 2.75 MB
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4 2.75 MB
Part 01-Module 01-Lesson 06_Data Processing/02. M1L4 02 Market Data V5-9aEp374GsgQ.mp4 2.73 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/09. DL 08 AND And OR Perceptrons-Y-ImuxNpS40.mp4 2.73 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/09. DL 08 AND And OR Perceptrons-Y-ImuxNpS40.mp4 2.73 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/01. 01 Intro-4C4PuJANIdE.mp4 2.73 MB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.mp4 2.73 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/08. M4 L2A 05 Covariance Matrix Of Factors V3-llA1A0vjSuI.mp4 2.72 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/05. KALMAN Gaussian Intro RENDER 1 1 V3-S2v1CExswT4.mp4 2.71 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/08. M4 L2A 19 Fama French SMB And HML V2-fnncnimScFc.mp4 2.71 MB
Part 02-Module 03-Lesson 06_Overlapping Labels/04. L6 05 HS Subsample Rows V4-IAl8kotkzYg.mp4 2.7 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 11 Node Importance Solution V1-Il0GsMMyTcM.mp4 2.7 MB
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.mp4 2.69 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.mp4 2.67 MB
Part 07-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4 2.65 MB
Part 02-Module 03-Lesson 01_Overview/03. Types Of Machine Learning Supervised V1-iGTuWwR6kZE.mp4 2.64 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 40 Wrapper Intro V1-IfwRHDSxwPs.mp4 2.63 MB
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.mp4 2.63 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/05. M4 L2b 05 Translating Between Bases V4-lrE4VOJ2RCA.mp4 2.63 MB
Part 01-Module 02-Lesson 05_Volatility/13. M2L5 13 Breakout Strategies V4-9eamk40DMu0.mp4 2.63 MB
Part 07-Module 01-Lesson 02_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.62 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/03. M4 L2A 02 Motivation For Risk Factor Model V2-jAQRjxK8PyQ.mp4 2.62 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/13. L4 14 Recap V1-e3qJYCQfJD0.mp4 2.61 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.mp4 2.61 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.59 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.59 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.mp4 2.57 MB
Part 07-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.mp4 2.57 MB
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.mp4 2.56 MB
Part 03-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.mp4 2.55 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.mp4 2.54 MB
Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.mp4 2.54 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/07. M8l2 07 Tcost Part2 V1-mWMFwCkWEFk.mp4 2.54 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/26. KALMAN QUIZ Kalman Prediction V1-d8Gx4-RghD0.mp4 2.53 MB
Part 04-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.mp4 2.52 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.mp4 2.52 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 60 Rank Sklearn V1-qZc-VBI4wY0.mp4 2.52 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.mp4 2.51 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/10. M4 L2b 10 Writing It Down Pt 3 V3-kSl0j4QIMIU.mp4 2.51 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 04b Project Reviews V1-KJbx9f9VKJE.mp4 2.5 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.mp4 2.5 MB
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.mp4 2.49 MB
Part 02-Module 03-Lesson 05_Feature Engineering/10. M7L5 24 Targets Solution V2-rC27Xoeyes0.mp4 2.48 MB
Part 06-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.mp4 2.48 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/16. L3 13 Summary V1-I7XKJf8t_0s.mp4 2.46 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 25 CaseC Intro V1-C6i12sqcgtY.mp4 2.45 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.mp4 2.45 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.mp4 2.43 MB
Part 02-Module 03-Lesson 05_Feature Engineering/08. M7L5 17 Sector Solution V1-I9iFbxzIu60.mp4 2.42 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 43 Prediction Solution Part 2 V1-y-vY_23kSU0.mp4 2.4 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/12. M4 L2b 13 Principal Components V3-XtecKk58CLs.mp4 2.38 MB
Part 02-Module 01-Lesson 03_Text Processing/03. Capturing Text Data-Z4mnMN1ApG4.mp4 2.37 MB
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.mp4 2.36 MB
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.mp4 2.36 MB
Part 01-Module 01-Lesson 05_Market Mechanics/06. M1L3 09 Open High Low Close V4-FgNY4YgVWFk.mp4 2.35 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/06. Accuracy-s6SfhPTNOHA.mp4 2.34 MB
Part 02-Module 03-Lesson 04_Random Forests/05. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.34 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/09. Experiment II-fq4eO7CybA4.mp4 2.31 MB
Part 02-Module 03-Lesson 05_Feature Engineering/05. M7L5 07 Volatility Dollar Volume Part 3 V1-xwBdEXXTu1s.mp4 2.3 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.mp4 2.3 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/15. Sequence-Batching-Z4OiyU0Cldg.mp4 2.29 MB
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.mp4 2.29 MB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/03. L3 03 Portfolio Mean V3-vozlctvug7I.mp4 2.27 MB
Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 13 Feature Importance Solution V1-C0_ngrOk-TA.mp4 2.26 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.26 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.26 MB
Part 02-Module 01-Lesson 04_Feature Extraction/01. Feature Extraction-Bd6TJB8eVLQ.mp4 2.26 MB
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.mp4 2.26 MB
Part 01-Module 01-Lesson 05_Market Mechanics/04. M1L3 04 Liquidity V4-KNVQeH6Y_YA.mp4 2.25 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.mp4 2.25 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.mp4 2.25 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/11. 06 Precision SC V1-q2wVorBfefU.mp4 2.24 MB
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.mp4 2.23 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/10. Answer False Negatives And Positives-KOytJL1lvgg.mp4 2.23 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/05. Learn Gate-aVHVI7ovbHY.mp4 2.22 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4 2.22 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.mp4 2.21 MB
Part 06-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.mp4 2.21 MB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.mp4 2.2 MB
Part 01-Module 01-Lesson 07_Stock Returns/01. M1L5 01 Intro V2-mE8OOxkgzy8.mp4 2.2 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.mp4 2.19 MB
Part 02-Module 03-Lesson 05_Feature Engineering/09. M7L5 19 Dates Intro Part 2 V1--I-jF0Ikli0.mp4 2.17 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/08. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.17 MB
Part 01-Module 03-Lesson 04_Portfolio Optimization/01. L4 01 Intro V1-CtIcmmR0YTs.mp4 2.17 MB
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.mp4 2.17 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/03. M4 L2A 14 Time Series Risk Model Specific Variance V2-I0uJLfh_OgQ.mp4 2.16 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.mp4 2.16 MB
Part 02-Module 03-Lesson 05_Feature Engineering/10. M7L5 23 Targets Intro V1-yaKHIvm7cXY.mp4 2.16 MB
Part 02-Module 05-Lesson 01_Intro to Backtesting/11. M8L1 15 Outro V1-mFk_HYJLF1w.mp4 2.16 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/02. M4 L2A 13 Time Series Risk Model Factor Exposure V4-WPBSMptBrfw.mp4 2.16 MB
Part 02-Module 03-Lesson 02_Decision Trees/15. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.16 MB
Part 07-Module 01-Lesson 04_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.16 MB
Part 02-Module 01-Lesson 03_Text Processing/08. Part-of-Speech Tagging-WFEu8bXI5OA.mp4 2.15 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/08. When Accuracy Wont Work-r0-O-gIDXZ0.mp4 2.15 MB
Part 01-Module 02-Lesson 03_Regression/09. M2L3 08 Heteroskedasticity V2-wias9OZ1tU4.mp4 2.15 MB
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.mp4 2.15 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/12. 07 Recall SC V1-0n5wUZiefkQ.mp4 2.15 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.mp4 2.14 MB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/03. Grammar-Jw3dA7xmoQ4.mp4 2.14 MB
Part 10-Module 01-Lesson 01_Intro to NLP/04. Grammar-Jw3dA7xmoQ4.mp4 2.14 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/15. Momentum-r-rYz_PEWC8.mp4 2.14 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.mp4 2.14 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.mp4 2.14 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/14. M4 L2A 25 Specific Variance V2-JwA9g3NBglE.mp4 2.14 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/14. KALMAN QUIZ Parameter Update 01 RENDER V3-UUXETqShme4.mp4 2.14 MB
Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.mp4 2.13 MB
Part 01-Module 01-Lesson 06_Data Processing/01. M1L4 01 Stock Data V2-sN0_IqmMGGA.mp4 2.12 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/01. M2L2 01 Intro V1-OGx1aYHMgbs.mp4 2.12 MB
Part 01-Module 02-Lesson 03_Regression/17. M2L3 15 Summary V1-n2VxcEcw0GY.mp4 2.11 MB
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.mp4 2.1 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.mp4 2.09 MB
Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.mp4 2.08 MB
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.mp4 2.08 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.08 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.08 MB
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.mp4 2.08 MB
Part 01-Module 02-Lesson 02_Outliers and Filtering/09. M2L2 08 Generating Robust Trading Signals V3-1ikkZmVkjl0.mp4 2.07 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.mp4 2.07 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/03. Exemplo de classificação-Dh625piH7Z0.mp4 2.07 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/03. Exemplo de classificação-Dh625piH7Z0.mp4 2.07 MB
Part 02-Module 01-Lesson 05_Financial Statements/26. AIT M5L4B 99 Summary V1-NgIufQFEHps.mp4 2.07 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.mp4 2.07 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/22. L1 25 Net Asset Value V2-hBnY2DmEFo4.mp4 2.06 MB
Part 06-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.mp4 2.05 MB
Part 02-Module 01-Lesson 04_Feature Extraction/03. TF-IDF-XZBiBIRcACE.mp4 2.05 MB
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.mp4 2.04 MB
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.mp4 2.03 MB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/08. M4 L3b 06 Winners And Losers In Momentum Investing V2-84ygzbLENbE.mp4 2.01 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.mp4 2 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/03. Testing-EeBZpb-PSac.mp4 2 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 56 Test3 V1-W_N9wjVnfBk.mp4 2 MB
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4 1.99 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 63 Local Global V1-8JiJ3R2F3Ng.mp4 1.99 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 1.98 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 1.98 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/02. M1L1 02 Interview W Jonathan V1-AeranuDRL7k.mp4 1.98 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/19. M4 L2b 19 Outro V1-nfVnAkndJCY.mp4 1.97 MB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 30 Shapley Solution V1-YmCSCA8Psgk.mp4 1.97 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/13. M4 L2A 09 Variance Of 2 Stocks Part 2 V4-tSMutw0f6OE.mp4 1.96 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/12. M8l2 14 N By N Matrix V3-qYAWhI8hk7U.mp4 1.96 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.mp4 1.96 MB
Part 01-Module 01-Lesson 05_Market Mechanics/11. M1L3 15 Outro V2-XVvfToYCsmo.mp4 1.96 MB
Part 02-Module 01-Lesson 03_Text Processing/07. Stop Word Removal-WAU_Ij0GJbw.mp4 1.96 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 42 Prediction Intro Part 1 V2-MLAnUO0BSr0.mp4 1.95 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 1.95 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 1.95 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.mp4 1.93 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 1.92 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 1.92 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 23 Absolute Returns V3-wbb6WSyXLdU.mp4 1.92 MB
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/11. M4 L2b 11 Writing It Down Pt 4 V3-7XO-syqIpCE.mp4 1.92 MB
Part 02-Module 03-Lesson 01_Overview/04. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.mp4 1.92 MB
Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.mp4 1.91 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/08. L1 09 Price Weighting V2-2SFbwJ19NhA.mp4 1.91 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.mp4 1.9 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.mp4 1.88 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.mp4 1.88 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.mp4 1.88 MB
Part 01-Module 04-Lesson 03_Risk Factor Models/11. M4 L2A 07 Taking Constants Out Of Variance And Covariance Optional V3-M9R9870m_o0.mp4 1.87 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 1.86 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 1.86 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.mp4 1.86 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/10. KALMAN QUIZ Shifting The Mean 01 RENDER 1 V2-gfBdoCFborg.mp4 1.84 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 04a Knowledge V1-lX_is8cq0Bg.mp4 1.84 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/11. M4 L2A 22 Cross Sectional Risk Model A Different Approach V2-LauZ7h4bgKE.mp4 1.84 MB
Part 07-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.mp4 1.83 MB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.mp4 1.82 MB
Part 07-Module 01-Lesson 02_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4 1.8 MB
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.mp4 1.8 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.mp4 1.79 MB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 52 Shap All V1 (1)--hYuszY7ffo.mp4 1.79 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.mp4 1.78 MB
Part 02-Module 01-Lesson 03_Text Processing/01. Text Processing-6LO6I5M18PQ.mp4 1.77 MB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/16. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4 1.75 MB
Part 01-Module 03-Lesson 02_ETFs/10. L2 13 Realigning ETF Share Prices V2-aRXJxjQQSiI.mp4 1.74 MB
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.mp4 1.74 MB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/07. Categorical Ordinal Nominal Data-k5bLaPGY2Vw.mp4 1.74 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.mp4 1.73 MB
Part 02-Module 02-Lesson 02_Training Neural Networks/16. Error Functions Around the World-34AAcTECu2A.mp4 1.73 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4 1.73 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4 1.73 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.72 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.72 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/10. Other Architectures-MsxFDuYlTuQ.mp4 1.71 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.mp4 1.69 MB
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.mp4 1.69 MB
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.mp4 1.69 MB
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.mp4 1.68 MB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-09-21-at-11.36.43-am.png 1.67 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.65 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.65 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/10. M8l2 11 Optimization Without Constraints Part1 V2-l8UOQTmyyUw.mp4 1.65 MB
Part 06-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.mp4 1.65 MB
Part 02-Module 05-Lesson 03_Attribution/09. M8L4 010 Outro V1-PNnSfMm-7-s.mp4 1.63 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/arpan-happy-results.png 1.62 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2017-03-17-at-5.09.53-pm.png 1.62 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.mp4 1.62 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/arpan-happy-emoji.png 1.62 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/04. 分类问题 2 -46PywnGa_cQ.mp4 1.62 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/04. 分类问题 2 -46PywnGa_cQ.mp4 1.62 MB
Part 06-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.mp4 1.61 MB
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.mp4 1.61 MB
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-lS5DfbsWH34.mp4 1.61 MB
Part 01-Module 02-Lesson 05_Volatility/14. M2L5 15 Outro V1-FMXL37CkTgg.mp4 1.6 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.mp4 1.6 MB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.mp4 1.6 MB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4 1.59 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.mp4 1.59 MB
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.mp4 1.58 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/arpan-happy-features.png 1.58 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/09. Putting It All Together-IF8FlKW-Zo0.mp4 1.58 MB
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.mp4 1.58 MB
Part 07-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.mp4 1.57 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.mp4 1.57 MB
Part 01-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.48.22-pm.png 1.57 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/25. L1 28 Open End Mutual Funds Handling Withdrawals V2-46NGAQHY-Mc.mp4 1.55 MB
Part 01-Module 04-Lesson 01_Factors/10. M4 L1A 09 Overview For Standardizing A Factor V3-0clT0lnrTrU.mp4 1.54 MB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/06. M1L1 MV 04 Study Groups V1-vmjk1EKR6mM.mp4 1.53 MB
Part 02-Module 05-Lesson 03_Attribution/01. M8L4 01 Intro V1-sIh09ScQXCQ.mp4 1.52 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.mp4 1.52 MB
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.mp4 1.51 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/08. LSTM 7 Use Gate-5Ifolm1jTdY.mp4 1.5 MB
Part 01-Module 02-Lesson 01_Quant Workflow/03. M2L1 02 Quant Workflow V3-lZfCCRv2rEE.mp4 1.5 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.49 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.49 MB
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.mp4 1.49 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.mp4 1.49 MB
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.mp4 1.49 MB
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.mp4 1.48 MB
Part 07-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.48 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.mp4 1.46 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.mp4 1.46 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.mp4 1.44 MB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/08. M2L6 11 Clustering Stocks V3-LkgCK_qPqWE.mp4 1.44 MB
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.mp4 1.42 MB
Part 02-Module 03-Lesson 05_Feature Engineering/06. M7L5 09 Dispersion Intro Part 2 V1-waeV2fKGeW8.mp4 1.41 MB
Part 07-Module 01-Lesson 02_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4 1.41 MB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 61 Rank Sklearn Solution V1-p6_reRrh3p0.mp4 1.39 MB
Part 01-Module 02-Lesson 03_Regression/14. M2L3 12 Multivariate Linear Regression V3-WbCGVF7SAN0.mp4 1.39 MB
Part 02-Module 01-Lesson 04_Feature Extraction/10. NLP Summary-B9ul8fsQYOA.mp4 1.38 MB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/arpan-shocked.png 1.38 MB
Part 01-Module 03-Lesson 02_ETFs/07. L2 09 Redeeming Shares V3-ZSVgU7DBarc.mp4 1.38 MB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/23. L1 26 Expense Ratios V2-SHZ0AhJq134.mp4 1.37 MB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.mp4 1.37 MB
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.mp4 1.35 MB
Part 01-Module 02-Lesson 04_Time Series Modeling/09. M2L4 11 Outro V1-6sheR92KUU8.mp4 1.35 MB
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.mp4 1.34 MB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/04. M4 L2A 15 Time Series Risk Model V2-Lz3RMLmov8o.mp4 1.34 MB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.33 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.33 MB
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Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.mp4 1.27 MB
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Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.mp4 1.21 MB
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Part 10-Module 01-Lesson 01_Intro to NLP/12. Modeling-P4w_2rkxBvE.mp4 1.19 MB
Part 02-Module 01-Lesson 03_Text Processing/09. Named Entity Recognition-QUQu2nsE7vE.mp4 1.17 MB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/14. M8l2 17 Objective Function Gradient V1-MXQTfFrZ44Y.mp4 1.17 MB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.13 MB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.13 MB
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Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.mp4 1.06 MB
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Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.mp4 1.04 MB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/06. Forget Gate-iWxpfxLUPSU.mp4 1.04 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.mp4 1.03 MB
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Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.mp4 1.01 MB
Part 06-Module 01-Lesson 05_Binomial Distribution/img/48713571.gif 1011.65 KB
Part 07-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4 1001.4 KB
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.mp4 991.73 KB
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.mp4 990.87 KB
Part 07-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.mp4 982.28 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/08. 为何是神经网络-zAkzOZntK6Y.mp4 982.27 KB
Part 07-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.mp4 981.31 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2017-03-17-at-5.25.16-pm.png 979.06 KB
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Part 02-Module 01-Lesson 03_Text Processing/13. Summary-zKYEvRd2XmI.mp4 977.95 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.mp4 954.56 KB
Part 02-Module 02-Lesson 02_Training Neural Networks/14. Learning Rate-TwJ8aSZoh2U.mp4 927.05 KB
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Part 02-Module 03-Lesson 01_Overview/img/screen-shot-2018-12-21-at-1.54.06-pm.png 898.85 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.mp4 893.3 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2017-11-07-at-2.17.08-pm.png 882.6 KB
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Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.mp4 863.99 KB
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Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.mp4 805.43 KB
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Part 07-Module 01-Lesson 03_Clustering/img/sebastian-katie-jay.png 779.77 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.mp4 771.83 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/12. KALMAN QUIZ Predicting The Peak 01 RENDER V1-_fGH3xJMxdM.mp4 768.11 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.mp4 753.05 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/student-quiz.png 748.98 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/student-quiz.png 748.98 KB
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.mp4 747.61 KB
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.mp4 747.17 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.mp4 719.39 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2018-04-23-at-4.10.20-pm.png 716.56 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/img/6509638772.gif 711.08 KB
Part 07-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.mp4 692.8 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/13. KALMAN QUIZ Predicting The Peak 02 RENDER V1-mcwr6FcP2Vc.mp4 677.46 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/07. Remember Gate-0qlm86HaXuU.mp4 676.91 KB
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.mp4 672.27 KB
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Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.mp4 661.1 KB
Part 07-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.mp4 660.25 KB
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.mp4 650.53 KB
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.mp4 639.69 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/models.png 627.96 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/and-to-or.png 606.14 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/and-to-or.png 606.14 KB
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.mp4 603.29 KB
Part 02-Module 03-Lesson 06_Overlapping Labels/06. L6 08 HS Ensemble Models Trained On Nonoverlapping Periods V5-YiXkL-Ts67I.mp4 601.84 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/profile-pics.jpg 595.62 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/15. KALMAN QUIZ Parameter Update 02 RENDER V2-vl6GkkEgY4M.mp4 574.57 KB
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Part 04-Module 01-Lesson 01_Introduction/img/grant.png 569.9 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.mp4 569.35 KB
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 44 Weight Intro V1-1cautGeQWDE.mp4 556.78 KB
Part 02-Module 03-Lesson 04_Random Forests/img/row-column-example-dataset.png 539.2 KB
Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors/img/screen-shot-2018-11-07-at-9.59.16-pm.png 529.19 KB
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.mp4 528.9 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.mp4 523 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2018-11-19-at-11.32.05-am.png 521.11 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.mp4 519.05 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/09. DL 09 XOR Perceptron--z9K49fdE3g.mp4 511.79 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/09. DL 09 XOR Perceptron--z9K49fdE3g.mp4 511.79 KB
Part 03-Module 01-Lesson 01_Why Python Programming/img/screen-shot-2018-03-19-at-2.30.59-pm.png 507.42 KB
Part 05-Module 01-Lesson 02_NumPy/img/screen-shot-2018-03-19-at-2.30.59-pm.png 507.42 KB
Part 05-Module 01-Lesson 03_Pandas/img/screen-shot-2018-03-19-at-2.30.59-pm.png 507.42 KB
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.mp4 506.06 KB
Part 07-Module 01-Lesson 01_Linear Regression/img/house.png 491.52 KB
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.mp4 484.71 KB
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.mp4 479.6 KB
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.mp4 473.3 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/screen-shot-2018-03-19-at-3.49.28-pm.png 471.61 KB
Part 02-Module 02-Lesson 02_Training Neural Networks/img/screen-shot-2018-03-19-at-3.49.28-pm.png 471.61 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/screen-shot-2018-03-19-at-3.49.28-pm.png 471.61 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.mp4 467.38 KB
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Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/img/6485174133.gif 458.07 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2017-03-17-at-4.03.20-pm.png 451.9 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/11. KALMAN QUIZ Shifting The Mean 02 RENDER V1-L8vNIKvpJ1s.mp4 451.32 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.mp4 447.99 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/img/6499079068.gif 445.94 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/img/6551597473.gif 444.36 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/screen-shot-2018-03-19-at-2.49.57-pm.png 442.46 KB
Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors/img/image4.png 436.47 KB
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.mp4 423.73 KB
Part 07-Module 01-Lesson 03_Clustering/img/3013998667.gif 404.61 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2018-04-23-at-4.14.19-pm.png 395.38 KB
Part 02-Module 02-Lesson 02_Training Neural Networks/10. Random Restart-idyBBCzXiqg.mp4 394.99 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/or-quiz.png 393.62 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/or-quiz.png 393.62 KB
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.mp4 390.91 KB
Part 01-Module 04-Lesson 01_Factors/img/screen-shot-2018-10-29-at-5.40.03-pm.png 389.4 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/img/mat-leonard-circle.png 384.91 KB
Part 01-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.49.50-pm.png 375.54 KB
Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors/img/screen-shot-2018-11-07-at-10.23.07-pm.png 366.06 KB
Part 04-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.14.23-pm.png 358.59 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-3.45.03-pm.png 351.54 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-3.22.39-pm.png 346.74 KB
Part 05-Module 01-Lesson 02_NumPy/img/screen-shot-2018-03-19-at-3.21.24-pm.png 339.9 KB
Part 05-Module 01-Lesson 03_Pandas/img/screen-shot-2018-03-19-at-3.21.24-pm.png 339.9 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.mp4 339.25 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/github-issues.png 336.83 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/talent-program.png 336.13 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/media/Markdown+cells.mp4 330.36 KB
index.html 327.93 KB
Part 04-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-26-at-11.05.49-pm.png 323.9 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.mp4 323.09 KB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/img/dancing-beemo.gif 318.16 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-02-10-at-8.59.39-pm.png 314.45 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.51.08-pm.png 313.39 KB
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.mp4 312.59 KB
Part 03-Module 01-Lesson 05_Scripting/img/generate-messages-output.png 310.53 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/img/48665990.gif 309.25 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2017-03-17-at-5.12.46-pm.png 305.85 KB
Part 02-Module 03-Lesson 02_Decision Trees/img/trees.png 300 KB
Part 07-Module 01-Lesson 04_Decision Trees/img/trees.png 300 KB
Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors/img/screen-shot-2018-11-09-at-6.28.07-pm.png 299.96 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/screen-shot-2018-09-10-at-7.38.39-pm.png 296.26 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/screen-shot-2018-09-10-at-7.38.39-pm.png 296.26 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-02-10-at-9.00.30-pm.png 295.89 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/img/48745039.gif 291.24 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2018-04-23-at-4.05.20-pm.png 289.67 KB
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.mp4 284.83 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/screen-shot-2018-08-20-at-4.07.31-pm.png 282 KB
Part 04-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.05.48-pm.png 279.73 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 260.01 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 260.01 KB
Part 01-Module 04-Lesson 01_Factors/img/screen-shot-2018-10-29-at-5.35.49-pm.png 255.4 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/precision-quiz.png 250.81 KB
Part 01-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.49.34-pm.png 238.98 KB
assets/js/katex.min.js 231.26 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/redacted-linkedinresults.png 230.77 KB
Part 07-Module 01-Lesson 03_Clustering/img/2956218691.gif 229.48 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/recall-quiz.png 228.26 KB
Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors/img/image8.png 228.06 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/profiles-view.png 223.53 KB
Part 01-Module 01-Lesson 02_Get Help from Peers and Mentors/img/screen-shot-2018-11-07-at-9.55.40-pm.png 222.17 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-01-30-at-5.14.39-pm.png 220.32 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.mp4 220.29 KB
Part 04-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-2.24.21-pm.png 218.72 KB
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.mp4 218.29 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-3.04.01-pm.png 217.46 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/media/notebook+interface.mp4 215.47 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/xor.png 214.95 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/xor.png 214.95 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/stop-sign-classification.png 211.5 KB
Part 02-Module 03-Lesson 04_Random Forests/img/example-finance-tree.png 209.85 KB
Part 02-Module 01-Lesson 01_Welcome To Term II/img/parnian-barekatain.jpg 209.57 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/meme.png 209.05 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/img/meme.png 209.05 KB
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Part 07-Module 01-Lesson 02_Naive Bayes/img/meme.png 209.05 KB
Part 07-Module 01-Lesson 03_Clustering/img/meme.png 209.05 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/meme.png 209.05 KB
Part 04-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-24-at-3.13.49-pm.png 204.57 KB
Part 07-Module 01-Lesson 03_Clustering/img/3081768538.gif 202.88 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-02-23-at-5.11.40-pm.png 200.67 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-3.51.18-pm.png 198.22 KB
Part 07-Module 01-Lesson 01_Linear Regression/img/batch-stochastic.png 196.92 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/img/screen-shot-2017-03-13-at-12.36.54-pm.png 193 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-2.59.42-pm.png 192.43 KB
Part 07-Module 01-Lesson 03_Clustering/img/3050028596.gif 192.14 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-3.37.30-pm.png 192.09 KB
Part 02-Module 03-Lesson 02_Decision Trees/img/table.png 192.08 KB
Part 07-Module 01-Lesson 04_Decision Trees/img/table.png 192.08 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/confusion.png 188.85 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/medical.png 186.53 KB
Part 07-Module 01-Lesson 03_Clustering/img/3056738546.gif 183.68 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/img/mat-headshot.png 179.99 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/img/mat-headshot.png 179.99 KB
Part 07-Module 01-Lesson 01_Linear Regression/img/quiz.jpg 174.18 KB
Part 07-Module 01-Lesson 03_Clustering/img/3034378634.gif 173.12 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2018-01-24-at-12.03.45-am.png 170.85 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/media/command+palette.mp4 169.16 KB
Part 01-Module 04-Lesson 01_Factors/img/screen-shot-2018-10-29-at-11.08.44-pm.png 164.9 KB
Part 07-Module 01-Lesson 03_Clustering/img/3004978616.gif 164.57 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/img/08-identify-pairs-to-trade.png 162.42 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.29.49-pm.png 160.71 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-2.48.16-pm.png 157.95 KB
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Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/magic-timeit.png 157.29 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/m4l2a-01-image-v1.png 157.09 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/server-shutdown.png 155.42 KB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/img/screen-shot-2018-11-05-at-11.33.57-am.png 153.88 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.32.19-pm.png 152.53 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-05-11-at-11.03.34-am.png 150.98 KB
Part 07-Module 01-Lesson 03_Clustering/img/3040398570.gif 148.74 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/email.png 148.53 KB
Part 02-Module 03-Lesson 02_Decision Trees/img/screen-shot-2018-12-12-at-12.49.37-pm.png 146.65 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-2.42.15-pm.png 145.49 KB
Part 01-Module 02-Lesson 03_Regression/img/screen-shot-2018-04-19-at-1.17.37-pm.png 142.44 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-2.28.21-pm.png 140.95 KB
Part 02-Module 03-Lesson 02_Decision Trees/img/recommending-apps.png 140.56 KB
Part 07-Module 01-Lesson 04_Decision Trees/img/recommending-apps.png 140.56 KB
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Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-5.00.26-pm.png 135.65 KB
Part 07-Module 01-Lesson 02_Naive Bayes/img/spamham.png 135.09 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.37.05-pm.png 126.82 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-2.47.08-pm.png 126.78 KB
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Part 07-Module 01-Lesson 03_Clustering/img/3058428551.gif 124.68 KB
Part 06-Module 01-Lesson 07_Bayes Rule/img/screen-shot-2017-08-02-at-11.49.10-am.png 120.26 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/img/screen-shot-2018-10-15-at-8.35.15-pm.png 119.96 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/img/screen-shot-2018-10-29-at-3.36.06-pm.png 119.81 KB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/img/screen-shot-2018-02-14-at-6.07.26-pm.png 117.44 KB
Part 02-Module 05-Lesson 03_Attribution/img/variance-decomp-2.png 115.22 KB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/img/screen-shot-2019-03-18-at-12.55.43-pm.png 114.95 KB
Part 04-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.57.42-pm.png 114.23 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.15.46-pm.png 113.47 KB
Part 04-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-24-at-2.27.07-pm.png 113.18 KB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/img/factor-cov.png 112.91 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.04.18-pm.png 111.86 KB
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Part 07-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-01-06-at-9.41.01-pm.png 110.7 KB
Part 01-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.38.47-pm.png 110.58 KB
Part 01-Module 02-Lesson 03_Regression/img/distributions.png 110.42 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.06.03-pm.png 109.97 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/conda-tab.png 109.92 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/learning-curves.png 109.03 KB
Part 06-Module 01-Lesson 07_Bayes Rule/img/screen-shot-2017-08-02-at-11.40.37-am.png 108.23 KB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/img/screen-shot-2019-03-18-at-12.55.32-pm.png 106.68 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/img/screen-shot-2018-10-29-at-4.20.49-pm.png 106.27 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/img/accuracy-quiz.png 105.85 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/img/screen-shot-2018-10-29-at-4.37.50-pm.png 105.28 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/img/screen-shot-2019-02-26-at-4.09.24-pm.png 104.9 KB
Part 01-Module 04-Lesson 01_Factors/img/screen-shot-2018-10-29-at-5.29.38-pm.png 104.85 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/img/screen-shot-2018-05-26-at-4.05.55-pm.png 104.63 KB
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Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/img/screen-shot-2018-02-14-at-3.59.39-pm.png 102.91 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/new-notebook.png 101.77 KB
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Part 01-Module 04-Lesson 03_Risk Factor Models/img/screen-shot-2018-10-29-at-2.48.34-pm.png 99.98 KB
Part 04-Module 01-Lesson 01_Introduction/img/screen-shot-2018-04-02-at-4.25.41-pm.png 97.56 KB
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Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/notebook-json.png 95.29 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/media/unnamed-project-desc-0.gif 94.58 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/xor-quiz.png 94.14 KB
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Part 04-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-23-at-11.30.13-am.png 92.79 KB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/img/factor-var.png 92.7 KB
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Part 05-Module 01-Lesson 01_Jupyter Notebooks/img/magic-matplotlib.png 90.72 KB
Part 04-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-2.28.03-pm.png 90.71 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/img/48721292.gif 90.54 KB
Part 01-Module 01-Lesson 07_Stock Returns/img/aapl-returns-log-returns.jpeg 90.07 KB
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Part 06-Module 01-Lesson 03_Admissions Case Study/img/48688787.gif 82.4 KB
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Part 01-Module 03-Lesson 03_Portfolio Risk and Return/img/var-diagram.jpg 77.34 KB
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Part 04-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-23-at-10.49.16-am.png 42.36 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/09. Perceptrons as Logical Operators.html 20.44 KB
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Part 02-Module 03-Lesson 07_Feature Importance/11. Tree Shap Code Walkthrough (Optional).html 18.42 KB
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Part 07-Module 01-Lesson 01_Linear Regression/15. Linear Regression in scikit-learn.html 17.35 KB
Part 03-Module 01-Lesson 05_Scripting/22. Quiz The Standard Library.html 17.32 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/12. [Preview] Project Mimic Me!.html 17.28 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent.html 17.26 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/36. Learning Objectives - Bayes' Rule.html 17.23 KB
Part 03-Module 01-Lesson 03_Control Flow/02. Conditional Statements.html 17.23 KB
Part 01-Module 01-Lesson 07_Stock Returns/04. Quiz Log Returns and Compounding.html 17.21 KB
Part 02-Module 03-Lesson 02_Decision Trees/21. Decision Trees in sklearn.html 17.16 KB
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Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/speaking.png 17.08 KB
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Part 03-Module 01-Lesson 03_Control Flow/13. Quiz For Loops.html 16.99 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/04. Notebook + Quiz Building Confidence Intervals.html 16.99 KB
Part 05-Module 01-Lesson 02_NumPy/08. Slicing ndarrays.html 16.98 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Algorithm.html 16.72 KB
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Part 03-Module 01-Lesson 02_Data Types and Operators/22. Lists and Membership Operators.html 16.66 KB
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Part 07-Module 01-Lesson 04_Decision Trees/17. Decision Trees in sklearn.html 16.5 KB
Part 01-Module 04-Lesson 06_Alpha Factors/11. Ranking in Zipline.html 16.46 KB
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Part 06-Module 01-Lesson 13_Case Study AB tests/06. Quiz Experiment I.html 16.37 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/Project Rubric - Improve Your LinkedIn Profile.html 16.34 KB
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Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/25. Quiz Shape and Outliers (Final Quiz).html 16.25 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/25. Quiz Connecting Errors and P-Values.html 15.94 KB
Part 03-Module 01-Lesson 03_Control Flow/07. Boolean Expressions for Conditions.html 15.93 KB
Part 01-Module 03-Lesson 04_Portfolio Optimization/06. Formulating Portfolio Optimization Problems.html 15.91 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/36. Quiz Compound Data Structures.html 15.88 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/18. Measures of Center (Mode).html 15.84 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/09. Notebook + Quiz Sampling Distributions Python.html 15.81 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/13. Quiz Types of Errors - Part III.html 15.77 KB
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Part 06-Module 01-Lesson 11_Confidence Intervals/img/screen-shot-2017-11-06-at-1.14.05-pm.png 15.64 KB
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Part 03-Module 01-Lesson 04_Functions/02. Defining Functions.html 15.49 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/17. Quiz Difficulties in AB Testing.html 15.46 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/26. Notebook + Quiz Drawing Conclusions.html 15.33 KB
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Part 03-Module 01-Lesson 05_Scripting/04. [For Windows] Configuring Git Bash to Run Python.html 15.28 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/39. Summary.html 15.26 KB
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Part 03-Module 01-Lesson 02_Data Types and Operators/23. Quiz Lists and Membership Operators.html 15.12 KB
Part 07-Module 01-Lesson 01_Linear Regression/19. (Optional) Closed form Solution Math.html 15.07 KB
Part 03-Module 01-Lesson 03_Control Flow/10. For Loops.html 15.03 KB
Part 03-Module 01-Lesson 03_Control Flow/29. Quiz Zip and Enumerate.html 14.98 KB
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1.html 14.98 KB
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Part 02-Module 05-Lesson 04_Project 8 Backtesting/Project Rubric - Backtesting.html 14.71 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/28. Quiz Notation for the Mean.html 14.69 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. Neural Network Architecture.html 14.3 KB
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Part 05-Module 01-Lesson 03_Pandas/06. Arithmetic Operations on pandas Series.html 14.25 KB
Part 01-Module 04-Lesson 01_Factors/12. Zipline Pipeline SC V1-DHTwIwVk_sc.en.vtt 14.24 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Neural Network Architecture.html 14.23 KB
Part 04-Module 01-Lesson 03_Linear Combination/06. Solving a Simplified Set of Equations.html 14.21 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/10. Quiz Types of Errors - Part II(a).html 14.08 KB
Part 02-Module 03-Lesson 07_Feature Importance/09. Shapley Code Walkthrough (Optional).html 14.05 KB
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Part 05-Module 01-Lesson 02_NumPy/09. Boolean Indexing, Set Operations, and Sorting.html 13.97 KB
Part 07-Module 01-Lesson 01_Linear Regression/17. Multiple Linear Regression.html 13.91 KB
Part 01-Module 04-Lesson 06_Alpha Factors/02. install libraries.html 13.89 KB
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Part 03-Module 01-Lesson 03_Control Flow/15. Quiz Match Inputs To Outputs.html 13.71 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Backpropagation.html 13.7 KB
assets/css/fonts/KaTeX_SansSerif-Regular.woff2 13.7 KB
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Part 03-Module 01-Lesson 02_Data Types and Operators/20. String Methods.html 13.37 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. Perceptron Trick.html 13.29 KB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/12. Training the Model.html 13.24 KB
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Part 01-Module 04-Lesson 06_Alpha Factors/21. get_clean_factor_and_forward_returns.html 13.16 KB
Part 01-Module 04-Lesson 06_Alpha Factors/35. The Fundamental Law of Active Management Part 2.html 13.1 KB
Part 02-Module 01-Lesson 01_Welcome To Term II/img/brok-bucholtz-thumbnail.jpg 13.05 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/02. What are Jupyter notebooks.html 13.01 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/18. Notebook + Quiz Central Limit Theorem.html 13.01 KB
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Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/24. Notebook + Quiz Bootstrapping.html 12.86 KB
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Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/01. Welcome!.html 12.8 KB
Part 01-Module 04-Lesson 06_Alpha Factors/17. Smoothing Quiz 1.html 12.79 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/13. Quiz Notation.html 12.75 KB
Part 06-Module 01-Lesson 08_Python Probability Practice/06. Cancer Test Results.html 12.74 KB
Part 01-Module 01-Lesson 07_Stock Returns/05. Distributions of Returns and Prices.html 12.72 KB
Part 01-Module 04-Lesson 06_Alpha Factors/43. mean returns by quantile quiz.html 12.72 KB
Part 03-Module 01-Lesson 05_Scripting/12. Errors and Exceptions.html 12.69 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/30. Quiz Sets.html 12.69 KB
Part 01-Module 04-Lesson 06_Alpha Factors/25. Making dollar neutral and leverage ratio equal to one.html 12.61 KB
Part 03-Module 01-Lesson 03_Control Flow/25. Break, Continue.html 12.54 KB
Part 01-Module 02-Lesson 03_Regression/03. Exercise Visualize Distributions.html 12.52 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/03. Testing your models.html 12.52 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Video Notation for Parameters vs. Statistics.html 12.52 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/16. Text Measures of Center and Spread Summary.html 12.51 KB
Part 01-Module 04-Lesson 06_Alpha Factors/32. Quiz factor_information_coefficient.html 12.49 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/22. Quiz Calculating a p-value.html 12.48 KB
Part 02-Module 01-Lesson 01_Welcome To Term II/img/ritter-gordon.jpg 12.47 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/31. Dictionaries and Identity Operators.html 12.46 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/14. The Capital Assets Pricing Model.html 12.42 KB
Part 02-Module 01-Lesson 01_Welcome To Term II/05. AITND Term II Interview W Justin V2 V2-JOkwa1brNX8.en.vtt 12.41 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/23. Quiz Introduction to Notation.html 12.41 KB
Part 01-Module 03-Lesson 04_Portfolio Optimization/07. cvxpy.html 12.4 KB
Part 06-Module 01-Lesson 07_Bayes Rule/34. Learning from Sensor Data.html 12.38 KB
Part 01-Module 04-Lesson 06_Alpha Factors/04. Definition of key words.html 12.34 KB
Part 01-Module 04-Lesson 06_Alpha Factors/22. Factor and forward returns exercise.html 12.26 KB
Part 01-Module 04-Lesson 06_Alpha Factors/47. Transfer Coefficient Coding Exercise.html 12.25 KB
Part 01-Module 04-Lesson 06_Alpha Factors/44. Quantile analysis exercise.html 12.25 KB
Part 01-Module 04-Lesson 06_Alpha Factors/33. Rank IC coding exercise.html 12.24 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Video Summation.html 12.24 KB
Part 01-Module 04-Lesson 06_Alpha Factors/26. Factor returns coding exercise.html 12.23 KB
Part 01-Module 04-Lesson 06_Alpha Factors/28. Sharpe Ratio Coding Exercise.html 12.23 KB
Part 01-Module 04-Lesson 06_Alpha Factors/08. Sector Neutral Exercise.html 12.21 KB
Part 01-Module 04-Lesson 06_Alpha Factors/40. Turnover Exercise.html 12.2 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/10. Booleans, Comparison Operators, and Logical Operators.html 12.2 KB
Part 01-Module 04-Lesson 06_Alpha Factors/18. Smoothing Exercise.html 12.2 KB
Part 01-Module 04-Lesson 06_Alpha Factors/15. z-score exercise.html 12.19 KB
Part 01-Module 04-Lesson 06_Alpha Factors/12. Ranking exercise.html 12.18 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/27. Quiz Summation.html 12.17 KB
Part 01-Module 04-Lesson 06_Alpha Factors/14. z-score quiz.html 12.16 KB
Part 03-Module 01-Lesson 03_Control Flow/32. Quiz List Comprehensions.html 12.16 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/11. Quiz Booleans, Comparison Operators, and Logical Operators.html 12.15 KB
Part 03-Module 01-Lesson 05_Scripting/20. Importing Local Scripts.html 12.13 KB
assets/css/fonts/KaTeX_Size2-Regular.ttf 12.12 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/10. PyTorch V2 Part 2 Solution 2 V1-8KRX7HvqfP0.en.vtt 12.12 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Video Capital vs. Lower.html 12.1 KB
Part 01-Module 04-Lesson 06_Alpha Factors/42. Quantile Analysis Part 2.html 12.1 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/11. PyTorch V2 Part 3 V1-9ILiZwbi9dA.zh-CN.vtt 12.07 KB
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Part 03-Module 01-Lesson 03_Control Flow/17. Iterating Through Dictionaries with For Loops.html 12.05 KB
assets/css/fonts/KaTeX_Script-Regular.woff2 11.99 KB
Part 03-Module 01-Lesson 05_Scripting/13. Handling Errors.html 11.99 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/13. Details of Johansen Test (optional).html 11.98 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/12. PyTorch V2 Part 3 Solution V2-zBWlOeX2sQM.zh-CN.vtt 11.96 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Cross-Entropy 2.html 11.96 KB
Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test.html 11.95 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/11. Time Management.html 11.95 KB
Part 03-Module 01-Lesson 03_Control Flow/09. Solution Boolean Expressions for Conditions.html 11.94 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Cross-Entropy 2.html 11.92 KB
Part 01-Module 04-Lesson 06_Alpha Factors/29. Halfway There!.html 11.91 KB
Part 03-Module 01-Lesson 03_Control Flow/21. Practice While Loops.html 11.9 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/19. What is a p-value Anyway.html 11.86 KB
assets/css/fonts/KaTeX_Caligraphic-Bold.woff 11.85 KB
Part 01-Module 04-Lesson 06_Alpha Factors/01. Intro Efficient Market hypothesis and Arbitrage opportunities.html 11.85 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/20. Video Random Variables.html 11.83 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/18. iVol Value and Idiosyncratic volatility Overview.html 11.82 KB
Part 01-Module 04-Lesson 06_Alpha Factors/06. Controlling for Risk within an Alpha Factor Part 1.html 11.82 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Video + Quiz Introduction to Sampling Distributions Part I.html 11.81 KB
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities.html 11.8 KB
Part 01-Module 04-Lesson 06_Alpha Factors/24. Return Denominator, Leverage, and Factor Returns.html 11.8 KB
Part 01-Module 04-Lesson 06_Alpha Factors/34. The Fundamental Law of Active Management Part 1.html 11.8 KB
Part 01-Module 04-Lesson 06_Alpha Factors/07. Controlling for Risk within an Alpha Factor Part 2.html 11.79 KB
Part 01-Module 04-Lesson 06_Alpha Factors/38. Turnover as a Proxy for Real World Constraints.html 11.78 KB
Part 01-Module 04-Lesson 06_Alpha Factors/30. Ranked Information Coefficient (Rank IC) Part 1.html 11.77 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/07. Solution Variables and Assignment Operators.html 11.76 KB
Part 01-Module 04-Lesson 06_Alpha Factors/31. Ranked Information Coefficient (Rank IC) Part 2.html 11.76 KB
Part 01-Module 04-Lesson 06_Alpha Factors/37. Real World Constraints Transaction Costs.html 11.76 KB
Part 01-Module 04-Lesson 06_Alpha Factors/45. Quantiles Academic Research vs. Practitioners.html 11.76 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood.html 11.75 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/04. Setting Up Hypotheses.html 11.75 KB
Part 01-Module 04-Lesson 06_Alpha Factors/05. Researching Alphas from Academic Papers.html 11.75 KB
Part 01-Module 04-Lesson 06_Alpha Factors/03. Alpha Factors versus Risk Factor Modeling.html 11.74 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/08. Quiz Types of Errors - Part I.html 11.73 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/07. ADF and roots.html 11.73 KB
Part 03-Module 01-Lesson 05_Scripting/24. Techniques for Importing Modules.html 11.72 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/27. Other Things to Consider - Impact of Large Sample Size.html 11.72 KB
Part 06-Module 01-Lesson 08_Python Probability Practice/07. Conditional Probability Bayes Rule Quiz.html 11.72 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood.html 11.72 KB
Part 01-Module 04-Lesson 06_Alpha Factors/51. Interlude Reading Academic Research Papers, Part 1.html 11.71 KB
Part 01-Module 04-Lesson 06_Alpha Factors/52. Interlude Reading Academic Research Papers, Part 2.html 11.71 KB
Part 01-Module 04-Lesson 06_Alpha Factors/39. Factor Rank Autocorrelation (Turnover).html 11.71 KB
Part 01-Module 04-Lesson 06_Alpha Factors/36. Real World Constraints Liquidity.html 11.71 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/03. Quiz Descriptive vs. Inferential (Udacity Students).html 11.71 KB
Part 03-Module 01-Lesson 05_Scripting/02. Python Installation.html 11.7 KB
Part 05-Module 01-Lesson 02_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.en.vtt 11.69 KB
Part 03-Module 01-Lesson 03_Control Flow/30. Solution Zip and Enumerate.html 11.68 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Video + Quiz Introduction to Sampling Distributions Part II.html 11.68 KB
Part 01-Module 04-Lesson 06_Alpha Factors/23. Universe construction rule.html 11.67 KB
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability.html 11.65 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous.html 11.64 KB
Part 01-Module 04-Lesson 06_Alpha Factors/41. Quantile Analysis Part 1.html 11.64 KB
Part 01-Module 04-Lesson 06_Alpha Factors/46. Transfer Coefficient.html 11.63 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses.html 11.62 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous.html 11.61 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests.html 11.6 KB
Part 01-Module 04-Lesson 06_Alpha Factors/48. It’s all Relative.html 11.59 KB
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior.html 11.59 KB
Part 01-Module 04-Lesson 06_Alpha Factors/49. Conditional Factors.html 11.59 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/08. Winners and Losers in Momentum Investing.html 11.59 KB
assets/css/fonts/KaTeX_Caligraphic-Regular.woff 11.59 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/17. Text Recap + Next Steps.html 11.58 KB
Part 01-Module 04-Lesson 06_Alpha Factors/19. Factor Returns.html 11.57 KB
Part 01-Module 04-Lesson 06_Alpha Factors/10. Ranking Part 2.html 11.57 KB
Part 01-Module 04-Lesson 06_Alpha Factors/27. Sharpe Ratio.html 11.57 KB
Part 01-Module 04-Lesson 06_Alpha Factors/09. Ranking Part 1.html 11.57 KB
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1.html 11.57 KB
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8.html 11.57 KB
Part 01-Module 04-Lesson 06_Alpha Factors/53. Interlude Reading Academic Research Papers, Part 3.html 11.56 KB
Part 01-Module 04-Lesson 06_Alpha Factors/50. Summary.html 11.56 KB
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Part 01-Module 04-Lesson 06_Alpha Factors/16. Smoothing.html 11.54 KB
Part 01-Module 04-Lesson 06_Alpha Factors/13. Z score.html 11.53 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/27. Tuples.html 11.46 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypothesis Tests - Part II.html 11.46 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/04. Launching the notebook server.html 11.45 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/35. Compound Data Structures.html 11.43 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/21. Another String Method - Split.html 11.42 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/14. Measures of Center (Mean).html 11.42 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/30. Text Descriptive vs. Inferential Summary.html 11.41 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/20. 07 CharRNN Solution V1-ed33qePHrJM.en.vtt 11.4 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/12. Metric - Average Classroom Time.html 11.38 KB
Part 03-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate.html 11.38 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Video Important Final Points.html 11.37 KB
Part 03-Module 01-Lesson 05_Scripting/27. Experimenting with an Interpreter.html 11.36 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/13. Other Language Associated with Confidence Intervals.html 11.36 KB
Part 03-Module 01-Lesson 05_Scripting/25. Quiz Techniques for Importing Modules.html 11.33 KB
Part 07-Module 01-Lesson 01_Linear Regression/14. Absolute Error vs Squared Error.html 11.33 KB
Part 02-Module 03-Lesson 07_Feature Importance/05. sklearn Code Walkthrough (Optional).html 11.31 KB
Part 03-Module 01-Lesson 03_Control Flow/11. Practice For Loops.html 11.3 KB
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6.html 11.27 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/05. Quiz 5 Number Summary Practice.html 11.26 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/06. Cointegration.html 11.26 KB
Part 03-Module 01-Lesson 03_Control Flow/31. List Comprehensions.html 11.26 KB
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability.html 11.26 KB
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5.html 11.25 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/29. Sets.html 11.25 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/29. Text Summary on Notation.html 11.25 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/07. Profile Essentials.html 11.25 KB
Part 02-Module 03-Lesson 02_Decision Trees/03. Recommending Apps 1.html 11.24 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/19. Maximizing Probabilities.html 11.21 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/04. Video Introduction to Five Number Summary.html 11.19 KB
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test.html 11.19 KB
Part 03-Module 01-Lesson 03_Control Flow/20. While Loops.html 11.18 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/10. Metric - Enrollment Rate.html 11.18 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/19. Maximizing Probabilities.html 11.17 KB
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3.html 11.17 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/26. Pre-Notebook Gradient Descent.html 11.17 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/26. Pre-Notebook Gradient Descent.html 11.17 KB
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2.html 11.17 KB
Part 06-Module 01-Lesson 08_Python Probability Practice/03. Probability Quiz.html 11.16 KB
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1.html 11.16 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/12. The Sharpe Ratio.html 11.16 KB
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3.html 11.16 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypothesis Tests - Part I.html 11.15 KB
Part 01-Module 04-Lesson 08_Advanced Portfolio Optimization/09. Alternative Ways of Setting Up the Problem.html 11.14 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/24. Solution List and Membership Operators.html 11.14 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/12. Types of Errors - Part III.html 11.14 KB
Part 01-Module 01-Lesson 08_Momentum Trading/11. Quiz Test Returns for Statistical Significance.html 11.14 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/19. Video What is Notation.html 11.14 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. Relative and Absolute Returns.html 11.13 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/20. 07 CharRNN Solution V1-ed33qePHrJM.pt-BR.vtt 11.12 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Video Shape.html 11.11 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/02. Mean Reversion.html 11.09 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/23. Logistic Regression.html 11.08 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/21. Quiz Variable Types.html 11.07 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/02. install libraries.html 11.06 KB
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2.html 11.06 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Get Opportunities with LinkedIn.html 11.06 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/16. Type and Type Conversion.html 11.05 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/23. Logistic Regression.html 11.05 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Video Working With Outliers My Advice.html 11.04 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/09. Types of Errors - Part II.html 11.04 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/07. Quiz Variance and Preferred Gaussian.html 11.03 KB
Part 05-Module 01-Lesson 03_Pandas/11. Manipulate a DataFrame.html 11.02 KB
assets/css/fonts/KaTeX_Size4-Regular.ttf 11.02 KB
Part 06-Module 01-Lesson 08_Python Probability Practice/05. Binomial Distributions Quiz.html 11.01 KB
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6.html 10.99 KB
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5.html 10.99 KB
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7.html 10.99 KB
Part 03-Module 01-Lesson 03_Control Flow/14. Solution For Loops Quiz.html 10.99 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/01. Prove Your Skills With GitHub.html 10.98 KB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/05. Multiplication of a Square Matrices.html 10.98 KB
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2.html 10.97 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile.html 10.97 KB
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3.html 10.96 KB
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4.html 10.96 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/14. Skewness and Momentum Attentional Bias.html 10.94 KB
Part 05-Module 01-Lesson 03_Pandas/04. Creating pandas Series.html 10.93 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem.html 10.93 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/03. AB Testing.html 10.92 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/14. Log-loss Error Function.html 10.91 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators.html 10.91 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/14. Log-loss Error Function.html 10.89 KB
Part 03-Module 01-Lesson 03_Control Flow/03. Practice Conditional Statements.html 10.89 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/03. Overnight Returns Abstract.html 10.89 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Video Two Useful Theorems - Law of Large Numbers.html 10.88 KB
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4.html 10.87 KB
Part 07-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.en.vtt 10.87 KB
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Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Video Descriptive vs. Inferential Statistics.html 10.86 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Video Bootstrapping The Central Limit Theorem.html 10.86 KB
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Part 02-Module 03-Lesson 02_Decision Trees/24. Visualizing Your Tree.html 10.85 KB
Part 02-Module 03-Lesson 02_Decision Trees/06. Tree Anatomy.html 10.84 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/28. Other Things to Consider - What If We Test More Than Once.html 10.84 KB
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Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/07. Matrix Multiplication - General.html 10.81 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/img/sml-chart.png 10.78 KB
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Part 03-Module 01-Lesson 05_Scripting/09. Quiz Scripting with Raw Input.html 10.75 KB
Part 03-Module 01-Lesson 05_Scripting/14. Practice Handling Input Errors.html 10.74 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/17. 04 Implementing CharRNN V2-MMtgZXzFB10.pt-BR.vtt 10.73 KB
Part 03-Module 01-Lesson 03_Control Flow/04. Solution Conditional Statements.html 10.72 KB
Part 01-Module 03-Lesson 04_Portfolio Optimization/04. Two-Asset Portfolio Optimization.html 10.71 KB
Part 01-Module 01-Lesson 06_Data Processing/09. Fundamental Information.html 10.7 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/13. Other Risk Measures.html 10.7 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer.html 10.69 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/16. Measures of Center (Median).html 10.68 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/33. Feedforward.html 10.67 KB
Part 04-Module 01-Lesson 01_Introduction/05. Working with Equations.html 10.67 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/17. Video Simulating from the Null.html 10.66 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/09. Build and Strengthen Your Network.html 10.65 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/09. Magic keywords.html 10.64 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/10. Text Sampling Distribution Notes.html 10.64 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/33. Feedforward.html 10.64 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction.html 10.63 KB
Part 03-Module 01-Lesson 05_Scripting/03. Install Python Using Anaconda.html 10.62 KB
Part 07-Module 01-Lesson 04_Decision Trees/02. Recommending Apps 1.html 10.59 KB
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Part 03-Module 01-Lesson 05_Scripting/06. Programming Environment Setup.html 10.54 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values.html 10.54 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Video Measures of Center (Mean).html 10.53 KB
Part 03-Module 01-Lesson 03_Control Flow/26. Quiz Break, Continue.html 10.52 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/03. Classification Problems 1.html 10.5 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/25. PyTorch V2 Part 8 Solution V1-4n6T93hKRD4.zh-CN.vtt 10.5 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/21. Solution Detecting Overfitting and Underfitting.html 10.49 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/12. 02 Time Series Prediction V2-xV5jHLFfJbQ.pt-BR.vtt 10.48 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/03. Classification Problems 1.html 10.48 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Video Working With Outliers.html 10.48 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Video The Shape For Data In The World.html 10.48 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/22. Multi-Class Cross Entropy.html 10.47 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Video Two Useful Theorems - Central Limit Theorem.html 10.46 KB
Part 01-Module 04-Lesson 09_Project 4 Alpha Research and Factor Modeling/Project Rubric - Multi-factor Model.html 10.46 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/06. Higher Dimensions.html 10.45 KB
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Part 01-Module 03-Lesson 04_Portfolio Optimization/02. What is Optimization.html 9.97 KB
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Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/08. Work Experiences Accomplishments.html 9.7 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/28. Perceptron vs Gradient Descent.html 9.66 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/28. Perceptron vs Gradient Descent.html 9.65 KB
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Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/01. Case Studies Intro.html 9.63 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/27. Notebook Gradient Descent.html 9.57 KB
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Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/13. Winners and Losers in Momentum Exercise.html 9.47 KB
Part 06-Module 01-Lesson 04_Probability/16. One Of Three 2.html 9.46 KB
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Part 03-Module 01-Lesson 04_Functions/08. Documentation.html 9.45 KB
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Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/06. Create Your Profile With SEO In Mind.html 9.45 KB
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Part 02-Module 03-Lesson 02_Decision Trees/04. Recommending Apps 2.html 9.26 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/01. Instructor.html 9.25 KB
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Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/03. Your Instructors.html 9.17 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions.html 9.12 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/03. Video Welcome!.html 9.12 KB
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1.html 9.12 KB
Part 07-Module 01-Lesson 04_Decision Trees/08. Entropy Formula 1.html 9.11 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/08. Calculate a Covariance Matrix.html 9.11 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Video Introduction to Summary Statistics.html 9.11 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/17. Solution New Mean and Variance.html 9.09 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction.html 9.09 KB
Part 06-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty.html 9.09 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/09. Winners and Losers Accelerated and Decelerated Gains and Losses.html 9.09 KB
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands).html 9.09 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/11. Precision.html 9.09 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3.html 9.08 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/02. Introduction.html 9.08 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/05. ScreenCast Difference In Means.html 9.08 KB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/01. What is a Matrix.html 9.07 KB
Part 06-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data.html 9.07 KB
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2.html 9.07 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/08. Answer Variance and Preferred Gaussian.html 9.07 KB
Part 03-Module 01-Lesson 03_Control Flow/34. Conclusion.html 9.05 KB
Part 02-Module 01-Lesson 03_Text Processing/06. Cleaning-qawXp9DPV6I.pt-BR.vtt 9.05 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/16. Skewness and Momentum Momentum Enhanced or weakened by Skew.html 9.05 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/12. Video Other Language Associated with Confidence Intervals.html 9.04 KB
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3.html 9.03 KB
Part 03-Module 01-Lesson 05_Scripting/21. The Standard Library.html 9.03 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3.html 9.03 KB
Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization.html 9.03 KB
Part 07-Module 01-Lesson 01_Linear Regression/02. Quiz Housing Prices.html 9.03 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/10. Winners and Losers approximating curves with polynomials.html 9.02 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/04. Terminology.html 9.02 KB
Part 03-Module 01-Lesson 04_Functions/11. Lambda Expressions.html 9.02 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/24. iVol Joint Factor Volatility Enhanced Price Earnings Ratio.html 9.01 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/04. Video What is Data Why is it important.html 8.99 KB
Part 05-Module 01-Lesson 02_NumPy/03. Why Use NumPy.html 8.99 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics.html 8.99 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Introduce Instructors.html 8.99 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/22. iVol Value, Fundamental or Discretionary Investing.html 8.98 KB
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Video Data Types Summary.html 8.98 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/10. 8 Word2vec Model V2-7BEYWhym8lI.zh-CN.vtt 8.98 KB
Part 06-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion.html 8.97 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/37. Outro.html 8.97 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/37. Outro.html 8.97 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/23. Kalman Filter Code.html 8.97 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited.html 8.97 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/06. 4 Data Subsampling V1-7SJXv2BQzZA.zh-CN.vtt 8.96 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2.html 8.96 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/25. NLP used to enhance Fundamental Analysis.html 8.96 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/06. Data Subsampling.html 8.96 KB
Part 03-Module 01-Lesson 05_Scripting/10. Solution Scripting with Raw Input.html 8.96 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/19. iVol Arbitrage and Efficient Pricing of Stocks.html 8.95 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/04. Answer Tracking Intro.html 8.95 KB
Part 02-Module 01-Lesson 01_Welcome To Term II/img/luis-serrano.jpg 8.95 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/02. AB Testing.html 8.95 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/12. Winners and Losers Creating a joint factor.html 8.95 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/17. Skewness and Momentum Conditional Factor.html 8.95 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/06. Overnight Returns Methods Quantile Analysis.html 8.94 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum.html 8.94 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/img/codecogseqn-60-2.png 8.94 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/img/codecogseqn-60-2.png 8.94 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/15. Active vs. Passive.html 8.94 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/05. Overnight Returns Data, Universe, Methods.html 8.94 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/04. Overnight Returns Possible Alpha Factors.html 8.94 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/15. Skewness and Momentum Defining Skew.html 8.93 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/12. Network Architectures Solution.html 8.92 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/10. Prepare for the Udacity Talent Program.html 8.92 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/21. 08 Making Predictions V3-BhrpV3kwATo.pt-BR.vtt 8.92 KB
Part 05-Module 01-Lesson 03_Pandas/07. Manipulate a Series.html 8.92 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/16. PyTorch V2 Part 5 V1 (1)-XACXlkIdS7Y.en.vtt 8.91 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/14. Video Correct Interpretations of Confidence Intervals.html 8.91 KB
Part 04-Module 01-Lesson 03_Linear Combination/04. Linear Combination -Quiz 1.html 8.91 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape.html 8.91 KB
Part 01-Module 01-Lesson 04_Stock Prices/02. Stock Prices.html 8.9 KB
Part 07-Module 01-Lesson 03_Clustering/13. Sklearn-3zHUAXcoZ7c.ar.vtt 8.9 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/25. iVol Generalizing the volatility Factor.html 8.9 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/26. Other Alternative Data.html 8.89 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/09. Statistical vs. Practical Significance.html 8.89 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/17. Cross Validation for Time Series.html 8.89 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/25. Answer 1D Kalman Filter.html 8.89 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/21. iVol Idiosyncratic Volatility.html 8.88 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/23. iVOL Quantamental Investing.html 8.88 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Answer Predict Function.html 8.87 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/08. Video Statistical vs. Practical Significance.html 8.87 KB
Part 07-Module 01-Lesson 01_Linear Regression/12. Mean vs Total Error.html 8.87 KB
Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/02. Pre-Notebook Sentiment RNN.html 8.86 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/09. Training a Model.html 8.85 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4.html 8.85 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/11. Risk Factors v. Alpha Factors part 4.html 8.84 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/20. iVol Arbitrage Risk.html 8.8 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/02. Introduction.html 8.8 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/11. Network Architectures in PyTorch.html 8.8 KB
Part 02-Module 03-Lesson 02_Decision Trees/16. Solution Information Gain.html 8.8 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function.html 8.8 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/11. Taking constants out of Variance and Covariance (optional).html 8.79 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/03. ScreenCast Sampling Distributions and Confidence Intervals.html 8.79 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/11. 9 Model Validation Loss V2-GKDCq8J76tM.en.vtt 8.79 KB
Part 03-Module 01-Lesson 05_Scripting/23. Solution The Standard Library.html 8.79 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/05. Single layer neural networks solution.html 8.79 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/06. Answer Gaussian Intro.html 8.79 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/10. Implementing Softmax Solution.html 8.78 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/14. Classifying Fashion-MNIST.html 8.78 KB
Part 02-Module 02-Lesson 02_Training Neural Networks/07. Regularization-ndYnUrx8xvs.pt-BR.vtt 8.78 KB
Part 07-Module 01-Lesson 03_Clustering/16. Counterintuitive Clusters.html 8.78 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/11. ScreenCast Traditional Confidence Interval Methods.html 8.77 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/21. 08 Making Predictions V3-BhrpV3kwATo.en.vtt 8.77 KB
Part 07-Module 01-Lesson 04_Decision Trees/05. Quiz Student Admissions.html 8.76 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias.html 8.76 KB
Part 02-Module 01-Lesson 05_Financial Statements/22. Searching by Class and Regexes.html 8.76 KB
Part 02-Module 01-Lesson 05_Financial Statements/21. Searching The Parse Tree.html 8.76 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/06. Networks Using Matrix Multiplication.html 8.75 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Use Your Story to Stand Out.html 8.75 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/16. Notebook New Mean and Variance.html 8.75 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/04. Single layer neural networks.html 8.75 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/05. Experiment I.html 8.75 KB
Part 02-Module 01-Lesson 05_Financial Statements/09. Searching For Simple Patterns.html 8.74 KB
Part 02-Module 01-Lesson 05_Financial Statements/20. Navigating The Parse Tree.html 8.74 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/10. Risk Factors v. Alpha Factors part 3.html 8.73 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/21. Notebook Predict Function.html 8.73 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/24. Notebook 1D Kalman Filter.html 8.73 KB
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means.html 8.73 KB
Part 02-Module 01-Lesson 05_Financial Statements/23. Children Tags.html 8.73 KB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/10. Optimization without constraints.html 8.73 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/14. Model Complexity Graph.html 8.73 KB
Part 02-Module 01-Lesson 05_Financial Statements/14. Substitutions and Flags.html 8.72 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/12. 02 Time Series Prediction V2-xV5jHLFfJbQ.zh-CN.vtt 8.72 KB
Part 02-Module 01-Lesson 05_Financial Statements/08. Finding MetaCharacters.html 8.72 KB
Part 03-Module 01-Lesson 05_Scripting/15. Solution Handling Input Errors.html 8.72 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2.html 8.72 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1.html 8.71 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/26. Summary.html 8.71 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2.html 8.71 KB
Part 03-Module 01-Lesson 05_Scripting/20. Importing Files-qjeSn6zZbR0.ar.vtt 8.71 KB
Part 02-Module 01-Lesson 05_Financial Statements/19. Parsing an HTML File.html 8.71 KB
Part 02-Module 01-Lesson 05_Financial Statements/11. Simple MetaCharacters.html 8.7 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/08. Neural Networks in PyTorch.html 8.7 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/16. Inference and Validation.html 8.7 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/09. Neural Networks Solution.html 8.7 KB
Part 04-Module 01-Lesson 01_Introduction/07. Try our workspace again!.html 8.68 KB
Part 04-Module 01-Lesson 04_Linear Transformation and Matrices/03. Matrix Addition Quiz.html 8.68 KB
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2.html 8.67 KB
Part 02-Module 01-Lesson 05_Financial Statements/10. Word Boundaries.html 8.67 KB
Part 02-Module 01-Lesson 05_Financial Statements/12. Character Sets.html 8.67 KB
Part 02-Module 01-Lesson 05_Financial Statements/13. Groups.html 8.67 KB
Part 02-Module 01-Lesson 05_Financial Statements/07. Finding Words .html 8.66 KB
Part 07-Module 01-Lesson 04_Decision Trees/09. Entropy Formula 2.html 8.65 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads.html 8.65 KB
Part 02-Module 01-Lesson 05_Financial Statements/06. Raw Strings.html 8.64 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads.html 8.64 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/27. Next Motion Models and State.html 8.64 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads.html 8.63 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/16. 12 CompleteModel CustomLoss V2-7SqNN_eUAdc.zh-CN.vtt 8.63 KB
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/06. Accuracy.html 8.63 KB
Part 02-Module 03-Lesson 05_Feature Engineering/05. Universal Quant Features.html 8.63 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3.html 8.63 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/09. Access the Career Portal.html 8.63 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2.html 8.63 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/23. Notebook Workspace w GPU.html 8.63 KB
Part 04-Module 01-Lesson 03_Linear Combination/08. Linear Combination - Quiz 3.html 8.63 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head.html 8.62 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/03. Notebook Workspace.html 8.62 KB
Part 07-Module 01-Lesson 04_Decision Trees/03. Recommending Apps 2.html 8.61 KB
Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/Project Rubric - Breakout Strategy.html 8.61 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2.html 8.6 KB
Part 10-Module 01-Lesson 01_Intro to NLP/05. Unstructured Text.html 8.59 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula.html 8.59 KB
Part 05-Module 01-Lesson 01_Jupyter Notebooks/11. Creating a slideshow.html 8.59 KB
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation.html 8.59 KB
Part 03-Module 01-Lesson 04_Functions/04. Solution Defining Functions.html 8.59 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements.html 8.58 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/11. Variations of Pairs Trading and Mean Reversion Trading.html 8.58 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/06. Variance of a 3-Asset Portfolio.html 8.58 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/20. PyTorch - Part 7-hFu7GTfRWks.zh-CN.vtt 8.56 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails.html 8.56 KB
Part 02-Module 03-Lesson 04_Random Forests/03. Ensemble Methods.html 8.56 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/07. Video Confidence Interval Applications.html 8.56 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/11. Boost Your Visibility.html 8.56 KB
Part 01-Module 02-Lesson 03_Regression/12. Breusch Pagan in Depth (Optional).html 8.56 KB
Part 01-Module 02-Lesson 03_Regression/05. Quiz Standard Normal Distribution.html 8.55 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4.html 8.55 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6.html 8.55 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/27. Interlude.html 8.55 KB
Part 03-Module 01-Lesson 05_Scripting/29. Conclusion.html 8.55 KB
Part 04-Module 01-Lesson 02_Vectors/10. Vectors- Quiz 2.html 8.55 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5.html 8.54 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3.html 8.54 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages.html 8.54 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4.html 8.54 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/16. Quiz Rate of Returns Over Multiple Periods.html 8.54 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/07. Index Categories.html 8.53 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Create Your Elevator Pitch.html 8.52 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/06. Ratios.html 8.52 KB
Part 06-Module 01-Lesson 04_Probability/20. Text Recap + Next Steps.html 8.52 KB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/04. Unstructured Text.html 8.51 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/01. Intro to RNNs.html 8.51 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial.html 8.51 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/22. Pre-Notebook with GPU.html 8.5 KB
Part 02-Module 03-Lesson 02_Decision Trees/22. Titanic Survival Model with Decision Trees.html 8.5 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability.html 8.5 KB
Part 02-Module 01-Lesson 05_Financial Statements/14. M5 SC 9 Substitutions And Flags V1-9pxTGOlkLEY.en.vtt 8.5 KB
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters.html 8.49 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/08. Context Word Targets.html 8.49 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/05. PyTorch V2 Part 1 Solution V1-mNJ8CujTtpo.zh-CN.vtt 8.49 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/08. Click Through Rate.html 8.48 KB
Part 03-Module 01-Lesson 04_Functions/16. [Optional] Solution Iterators and Generators.html 8.48 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/15. Answer Parameter Update.html 8.47 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.ja-JP.vtt 8.47 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.ja-JP.vtt 8.47 KB
Part 02-Module 03-Lesson 02_Decision Trees/23. [Solution] Titanic Survival Model.html 8.46 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/08. Price Weighting.html 8.46 KB
Part 02-Module 03-Lesson 02_Decision Trees/12. Entropy Formula 3.html 8.46 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/15. Quiz Portfolio Return with a 3-Asset Portfolio.html 8.45 KB
Part 01-Module 04-Lesson 01_Factors/11. Quiz dollar neutral and leverage ratio.html 8.45 KB
Part 02-Module 03-Lesson 05_Feature Engineering/09. Date Parts.html 8.45 KB
Part 04-Module 01-Lesson 03_Linear Combination/07. Linear Combination - Quiz 2.html 8.44 KB
Part 02-Module 03-Lesson 02_Decision Trees/25. Visualizing Your Tree Exercise.html 8.44 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/15. Price-Volume Factors.html 8.43 KB
Part 02-Module 03-Lesson 08_Project 7 Combining Signals for Enhanced Alpha/Project Rubric - Combining Signals for Enhanced Alpha.html 8.43 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/09. Fama French Risk Model.html 8.42 KB
Part 05-Module 01-Lesson 02_NumPy/08. NumPy 4 V1-jeU7lLgyMms.pt-BR.vtt 8.41 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/26. Close End Mutual Funds.html 8.4 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value.html 8.4 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value.html 8.4 KB
Part 02-Module 01-Lesson 05_Financial Statements/24. Exercise Get Headers and Paragraphs.html 8.4 KB
Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/07. Feedback.html 8.39 KB
Part 01-Module 03-Lesson 05_Project 3 Smart Beta and Portfolio Optimization/Project Rubric - Smart Beta and Portfolio Optimization.html 8.39 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/12. How an alpha factor becomes a risk factor part 1.html 8.39 KB
Part 02-Module 01-Lesson 05_Financial Statements/15. Applying Regexes to 10-Ks.html 8.37 KB
Part 07-Module 01-Lesson 02_Naive Bayes/11. Naive Bayes Algorithm 1.html 8.37 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/24. Open End Mutual Funds.html 8.36 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/24. Sentiment Analysis on News and Social Media.html 8.35 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/17. Text Recap + Next Steps.html 8.35 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/02. Intro to this lesson.html 8.35 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.zh-CN.vtt 8.34 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.zh-CN.vtt 8.34 KB
Part 02-Module 01-Lesson 05_Financial Statements/25. The Requests Library.html 8.34 KB
Part 04-Module 01-Lesson 02_Vectors/05. Transpose.html 8.32 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/19. Answer Gaussian Motion.html 8.32 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/13. Answer Predicting the Peak.html 8.32 KB
Part 02-Module 02-Lesson 05_Embeddings Word2Vec/11. 9 Model Validation Loss V2-GKDCq8J76tM.pt-BR.vtt 8.31 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/06. Covariance Matrix Using a Factor Model.html 8.31 KB
Part 01-Module 01-Lesson 08_Momentum Trading/10. The Many Meanings of Alpha.html 8.31 KB
Part 02-Module 01-Lesson 01_Welcome To Term II/img/juan-delgado-1.jpg 8.31 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/11. Answer Shifting the Mean.html 8.3 KB
Part 02-Module 01-Lesson 03_Text Processing/06. Cleaning-qawXp9DPV6I.en.vtt 8.29 KB
Part 03-Module 01-Lesson 04_Functions/09. Quiz Documentation.html 8.28 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects.html 8.28 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/03. Factor Returns as Latent Variables.html 8.27 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/07. Factor Models in Quant Finance.html 8.27 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions.html 8.26 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/26. Kalman Prediction.html 8.25 KB
Part 02-Module 03-Lesson 02_Decision Trees/08. Solution Student Admissions.html 8.24 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/21. Making Predictions.html 8.24 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/05. Factor Model Assumptions.html 8.24 KB
Part 02-Module 01-Lesson 05_Financial Statements/03. M5 SC 15 10Ks Walkthrough V1-0ytyZ4LVG6s.en.vtt 8.23 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/02. What is a Factor Model.html 8.23 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration.html 8.22 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/12. Time-Series Prediction.html 8.22 KB
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Part 02-Module 03-Lesson 02_Decision Trees/01. Welcome.html 8.2 KB
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Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/05. Size.html 7.38 KB
Part 07-Module 01-Lesson 02_Naive Bayes/14. Project.html 7.38 KB
Part 01-Module 02-Lesson 03_Regression/01. Intro.html 7.38 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Why Use an Elevator Pitch.html 7.38 KB
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Part 07-Module 01-Lesson 02_Naive Bayes/04. Guess the Person Now.html 7.37 KB
Part 01-Module 01-Lesson 08_Momentum Trading/08. Quiz Calculate Top and Bottom Performing.html 7.36 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/index.html 7.36 KB
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Part 09-Module 01-Lesson 01_Intro to Computer Vision/01. Welcome to Computer Vision.html 7.35 KB
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Part 01-Module 02-Lesson 05_Volatility/04. Scale of Volatility.html 7.26 KB
Part 02-Module 01-Lesson 03_Text Processing/04. Normalization.html 7.26 KB
Part 07-Module 01-Lesson 02_Naive Bayes/10. Bayesian Learning 3.html 7.26 KB
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Part 02-Module 03-Lesson 04_Random Forests/10. Choosing Hyperparameter Values.html 7.26 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion.html 7.26 KB
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Part 07-Module 01-Lesson 03_Clustering/03. Clustering Movies.html 7.23 KB
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Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/01. Welcome!.html 7.19 KB
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Part 01-Module 03-Lesson 02_ETFs/09. Arbitrage.html 7.13 KB
Part 06-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability.html 7.13 KB
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Part 01-Module 02-Lesson 01_Quant Workflow/05. Anatomy of a Strategy.html 7.12 KB
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Part 06-Module 01-Lesson 03_Admissions Case Study/12. Text Recap + Next Steps.html 7.1 KB
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Part 02-Module 03-Lesson 04_Random Forests/05. Perturbations on Rows.html 7.06 KB
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Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/06. The Core Idea.html 7.04 KB
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Part 02-Module 02-Lesson 02_Training Neural Networks/13. Batch vs Stochastic Gradient Descent.html 7.01 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt 3.34 KB
Part 01-Module 02-Lesson 06_Pairs Trading and Mean Reversion/02. M2L6 02 Mean Reversion V5-zQ08lFcZa_A.en.vtt 3.33 KB
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/14. Character-Wise RNN-dXl3eWCGLdU.en.vtt 3.33 KB
Part 01-Module 04-Lesson 06_Alpha Factors/41. M4 L3a 181 Quantile Analysis Part 1 V2-oT5GFbg0G8g.en.vtt 3.33 KB
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Part 06-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.pt-BR.vtt 3.31 KB
Part 02-Module 03-Lesson 04_Random Forests/06. L4 011 HS Random Forests V5-TSpYXdBYo1s.en.vtt 3.31 KB
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Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/01. MV 7 Transition To Project 02 1 V1-nkAcx2X_lfs.en.vtt 3.3 KB
Part 07-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt 3.3 KB
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Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.ar.vtt 3.29 KB
Part 01-Module 01-Lesson 05_Market Mechanics/09. M1L3 12 Gaps In Market Data V3-jMT3VbUGiZI.en.vtt 3.28 KB
Part 02-Module 01-Lesson 03_Text Processing/05. Tokenization-4Ieotbeh4u8.pt-BR.vtt 3.28 KB
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.en.vtt 3.28 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.en.vtt 3.28 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.ja.vtt 3.28 KB
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Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.ru.vtt 3.26 KB
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Part 06-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.en.vtt 3.23 KB
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Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.en.vtt 3.22 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/01. M4 L2A 12 Time Series Risk Model Factor Variance V2-hjVBXeZmA0w.en.vtt 3.22 KB
Part 01-Module 02-Lesson 07_Project 2 Breakout Strategy/04. MV 10 Transition From Project 02 Int V1-DYjOsL3VYfY.en.vtt 3.22 KB
Part 07-Module 01-Lesson 03_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.en.vtt 3.21 KB
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Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.es-MX.vtt 3.2 KB
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Part 03-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.pt-BR.vtt 2.68 KB
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Part 02-Module 02-Lesson 04_Recurrent Neural Networks/05. Learn Gate-aVHVI7ovbHY.pt-BR.vtt 2.66 KB
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Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/08. M4 L2A 19 Fama French SMB And HML V2-fnncnimScFc.en.vtt 2.65 KB
Part 01-Module 03-Lesson 02_ETFs/09. L2 11 2 Arbitrage Farmers Market V1-hHxp16mQNGA.en.vtt 2.64 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt 2.64 KB
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Part 05-Module 01-Lesson 01_Jupyter Notebooks/02. Jupyter-qiYDWFLyXvg.zh-CN.vtt 2.64 KB
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Part 02-Module 02-Lesson 04_Recurrent Neural Networks/05. Learn Gate-aVHVI7ovbHY.en.vtt 2.63 KB
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Part 03-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.ar.vtt 2.63 KB
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.en.vtt 2.63 KB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/09. Text Processing-pqheVyctkNQ.en.vtt 2.63 KB
Part 10-Module 01-Lesson 01_Intro to NLP/10. Text Processing-pqheVyctkNQ.en.vtt 2.63 KB
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Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.ar.vtt 2.61 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.ar.vtt 2.6 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.ar.vtt 2.6 KB
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Part 03-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.zh-CN.vtt 2.6 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.zh-CN.vtt 2.6 KB
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Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/10. M4 L2b 10 Writing It Down Pt 3 V3-kSl0j4QIMIU.en.vtt 2.59 KB
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Part 02-Module 01-Lesson 03_Text Processing/05. Tokenization-4Ieotbeh4u8.zh-CN.vtt 2.59 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt 2.59 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt 2.59 KB
Part 07-Module 01-Lesson 03_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.en.vtt 2.59 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/09. 09. Training a Model-m4GVfwVkj74.zh-CN.vtt 2.59 KB
Part 06-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.en.vtt 2.58 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.zh-CN.vtt 2.58 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.pt-BR.vtt 2.58 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.zh-CN.vtt 2.58 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.en.vtt 2.57 KB
Part 02-Module 03-Lesson 07_Feature Importance/13. M7L7 65 Rank Shap Solution V1-jkAxXatozUo.en.vtt 2.57 KB
Part 02-Module 05-Lesson 02_Optimization with Transaction Costs/07. M8l2 07 Tcost Part2 V1-mWMFwCkWEFk.en.vtt 2.57 KB
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/05. KALMAN Gaussian Intro RENDER 1 1 V3-S2v1CExswT4.zh-CN.vtt 2.57 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.pt-BR.vtt 2.57 KB
Part 01-Module 04-Lesson 06_Alpha Factors/37. M4 L3a 162 Real World Constraints Transaction Costs V2-HAif7xSh8z0.en.vtt 2.56 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.ja-JP.vtt 2.56 KB
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Part 06-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.th.vtt 2.56 KB
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Part 02-Module 02-Lesson 02_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt 2.55 KB
Part 07-Module 01-Lesson 03_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.pt-BR.vtt 2.55 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt 2.55 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt 2.55 KB
Part 04-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.pt-BR.vtt 2.55 KB
Part 03-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.en.vtt 2.54 KB
Part 03-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.pt-BR.vtt 2.54 KB
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Part 01-Module 01-Lesson 05_Market Mechanics/04. M1L3 04 Liquidity V4-KNVQeH6Y_YA.en.vtt 2.53 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/14. M4 L1B 13 Momentum Or Reversal V3-izTAHVF6V_g.en.vtt 2.53 KB
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Part 02-Module 03-Lesson 05_Feature Engineering/07. M7L5 13 Marketvol Intro V1-G03W42Z5RSo.en.vtt 2.52 KB
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Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.ar.vtt 2.52 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/03. Exemplo de classificação-Dh625piH7Z0.pt-BR.vtt 2.51 KB
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Part 07-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt 2.51 KB
Part 01-Module 02-Lesson 05_Volatility/06. M2L5 06 Rolling Windows V3-4EuMKqeNXA0.en.vtt 2.5 KB
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Part 02-Module 03-Lesson 02_Decision Trees/08. Student Admissions-TdgBi6LtOB8.en.vtt 2.5 KB
Part 03-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.zh-CN.vtt 2.5 KB
Part 07-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.en.vtt 2.5 KB
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Part 02-Module 02-Lesson 02_Training Neural Networks/15. Momentum-r-rYz_PEWC8.en.vtt 2.5 KB
Part 07-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.en.vtt 2.49 KB
Part 03-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.zh-CN.vtt 2.49 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/09. M4 L1B 08 Risk Factors V Alpha Factors Part 2 V2-AApfsuSpnMY.en.vtt 2.48 KB
Part 06-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.ar.vtt 2.48 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/09. DL 08 AND And OR Perceptrons-Y-ImuxNpS40.zh-CN.vtt 2.48 KB
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/09. DL 08 AND And OR Perceptrons-Y-ImuxNpS40.zh-CN.vtt 2.48 KB
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Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/27. L1 31 Transaction Costs V2-JGYAv7tQpyY.en.vtt 2.48 KB
Part 03-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.en.vtt 2.47 KB
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Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 41 Wrapper Solution V1-27tEa_Bpq20.en.vtt 2.46 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.pt-BR.vtt 2.45 KB
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Part 02-Module 02-Lesson 04_Recurrent Neural Networks/10. Other Architectures-MsxFDuYlTuQ.pt-BR.vtt 2.45 KB
Part 03-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.en.vtt 2.45 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.ja-JP.vtt 2.45 KB
Part 01-Module 04-Lesson 02_Factor Models and Types of Factors/05. M4 L1B 04 Factor Model Assumptions V3-qEu3m_3eGWk.en.vtt 2.44 KB
Part 06-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.pt-BR.vtt 2.44 KB
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Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/24. L1 27 OpenEnd Mutual Funds V2-T4_mmjEKUAo.en.vtt 2.3 KB
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Part 02-Module 03-Lesson 07_Feature Importance/05. M7L7 09 Gini Solution V1-xCjkhgQDTu4.en.vtt 2.3 KB
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Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/06. M4 L3b 05 Overnight Returns Methods Quantile Analysis V3-4Js3mghq2mU.en.vtt 2.3 KB
Part 09-Module 01-Lesson 01_Intro to Computer Vision/05. 05. Emotional Intelligence-D_LzJsJH5qk.en.vtt 2.3 KB
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/09. Text Processing-pqheVyctkNQ.zh-CN.vtt 2.3 KB
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Part 10-Module 01-Lesson 01_Intro to NLP/10. Text Processing-pqheVyctkNQ.zh-CN.vtt 2.3 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.ja-JP.vtt 2.29 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.zh-CN.vtt 2.28 KB
Part 01-Module 04-Lesson 07_Alpha Factor Research Methods/20. M4 L3b 16 IVol Arbitrage Risk V3-rKtJ3iAYYns.en.vtt 2.28 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.ar.vtt 2.28 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.pt-BR.vtt 2.28 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.ar.vtt 2.28 KB
Part 07-Module 01-Lesson 03_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.zh-CN.vtt 2.27 KB
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.ar.vtt 2.27 KB
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Part 07-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt 2.26 KB
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Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/14. PyTorch - Part 4-AEJV_RKZ7VU.en.vtt 2.26 KB
Part 01-Module 04-Lesson 06_Alpha Factors/03. M4 L3a 02 Alpha Factors Versus Risk Factor Modeling V2-qsahBvhVTkk.en.vtt 2.25 KB
Part 03-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.en.vtt 2.25 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.zh-CN.vtt 2.25 KB
Part 07-Module 01-Lesson 02_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt 2.24 KB
Part 03-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.ar.vtt 2.24 KB
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 32 Discussion Solution V1-pxmaMYOtNys.en.vtt 2.24 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/03. M4 L2A 14 Time Series Risk Model Specific Variance V2-I0uJLfh_OgQ.en.vtt 2.24 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt 2.23 KB
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Part 01-Module 02-Lesson 02_Outliers and Filtering/09. M2L2 08 Generating Robust Trading Signals V3-1ikkZmVkjl0.en.vtt 2.23 KB
Part 03-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.en.vtt 2.23 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.en.vtt 2.22 KB
Part 07-Module 01-Lesson 02_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt 2.22 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/11. M4 L2A 22 Cross Sectional Risk Model A Different Approach V2-LauZ7h4bgKE.en.vtt 2.22 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/02. L1 01 Stocks V2-XHo5iyxDxOQ.en.vtt 2.22 KB
Part 06-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.pt-BR.vtt 2.22 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt 2.21 KB
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Part 08-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt 2.21 KB
Part 02-Module 02-Lesson 02_Training Neural Networks/15. Momentum-r-rYz_PEWC8.zh-CN.vtt 2.21 KB
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Part 07-Module 01-Lesson 02_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt 2.21 KB
Part 06-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.pt-BR.vtt 2.21 KB
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Part 02-Module 03-Lesson 04_Random Forests/08. L4 15 HS Outofbag Score V4-CcdXrGYaOhE.en.vtt 2.21 KB
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/03. L3 03 Portfolio Mean V3-vozlctvug7I.en.vtt 2.21 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.ar.vtt 2.21 KB
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.ar.vtt 2.2 KB
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.th.vtt 2.2 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.en.vtt 2.2 KB
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.ar.vtt 2.19 KB
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Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/23. Kalman Filter Code - Artificial Intelligence for Robotics-3xBycKfnCOQ.es-ES.vtt 2.19 KB
Part 02-Module 02-Lesson 03_Deep Learning with PyTorch/14. PyTorch - Part 4-AEJV_RKZ7VU.pt-BR.vtt 2.19 KB
Part 06-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.ar.vtt 2.19 KB
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Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt 2.19 KB
Part 07-Module 01-Lesson 03_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.pt-BR.vtt 2.19 KB
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.ja-JP.vtt 2.18 KB
Part 01-Module 04-Lesson 04_Time Series and Cross Sectional Risk Models/14. M4 L2A 25 Specific Variance V2-JwA9g3NBglE.en.vtt 2.18 KB
Part 06-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.zh-CN.vtt 2.18 KB
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Part 03-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.zh-CN.vtt 2.18 KB
Part 01-Module 04-Lesson 03_Risk Factor Models/08. M4 L2A 05 Covariance Matrix Of Factors V3-llA1A0vjSuI.en.vtt 2.17 KB
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Part 02-Module 02-Lesson 06_Sentiment Prediction RNN/01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.en.vtt 2.17 KB
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Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt 2.16 KB
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Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.zh-CN.vtt 1.99 KB
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Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.pt-BR.vtt 1.97 KB
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Part 06-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.th.vtt 1.94 KB
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Part 02-Module 01-Lesson 03_Text Processing/07. Stop Word Removal-WAU_Ij0GJbw.pt-BR.vtt 1.79 KB
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Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 22 Relative Returns V2-m4MvYRlyPoU.en.vtt 1.77 KB
Part 03-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.en.vtt 1.77 KB
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Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 25 CaseC Intro V1-C6i12sqcgtY.en.vtt 1.77 KB
Part 02-Module 01-Lesson 03_Text Processing/03. Capturing Text Data-Z4mnMN1ApG4.en.vtt 1.77 KB
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Part 03-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.en.vtt 1.76 KB
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Part 06-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.pt-BR.vtt 1.75 KB
Part 06-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.zh-CN.vtt 1.75 KB
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Part 02-Module 02-Lesson 04_Recurrent Neural Networks/08. LSTM 7 Use Gate-5Ifolm1jTdY.en.vtt 1.75 KB
Part 01-Module 01-Lesson 08_Momentum Trading/13. M1L6 12 Finding Alpha V1-r8lfWVhfQC0.en.vtt 1.75 KB
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/28. L1 32 Summary V1-Pt2sVftdwS0.en.vtt 1.75 KB
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Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/04. Unstructured Text-OmwSdaec5vU.en.vtt 1.74 KB
Part 02-Module 04-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.zh-CN.vtt 1.74 KB
Part 10-Module 01-Lesson 01_Intro to NLP/05. Unstructured Text-OmwSdaec5vU.en.vtt 1.74 KB
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Part 03-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.ar.vtt 1.74 KB
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.pt-BR.vtt 1.73 KB
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Part 01-Module 03-Lesson 02_ETFs/03. L2 05 International ETFs V2-OL2p8S-82mY.en.vtt 1.73 KB
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Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.es-ES.vtt 1.72 KB
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Part 02-Module 02-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.ja-JP.vtt 1.71 KB
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Part 02-Module 01-Lesson 04_Feature Extraction/05. Word Embeddings-4mM_S9L2_JQ.pt-BR.vtt 1.71 KB
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Part 06-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.en.vtt 1.7 KB
Part 07-Module 01-Lesson 02_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt 1.7 KB
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Part 01-Module 01-Lesson 08_Momentum Trading/14. MV 13 Global Talent Is Equally Distributed V1-QwDJbbBl_48.en.vtt 1.3 KB
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Part 01-Module 03-Lesson 02_ETFs/03. L2 04 Commodity ETFs V2-UpgX6INJ6nU.en.vtt 996 B
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Part 02-Module 01-Lesson 03_Text Processing/13. Summary-zKYEvRd2XmI.zh-CN.vtt 984 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.ja.vtt 984 B
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Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.zh-CN.vtt 982 B
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Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.hr.vtt 957 B
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Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian - Artificial Intelligence for Robotics-fRYtUP0P4Lg.ja-JP.vtt 924 B
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Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.ja.vtt 902 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.ar.vtt 900 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.es-ES.vtt 900 B
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Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.pt-BR.vtt 899 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.en.vtt 898 B
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Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.zh-CN.vtt 896 B
Part 06-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.ar.vtt 894 B
Part 06-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.zh-CN.vtt 892 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/17. New Mean and Variance Solution - Artificial Intelligence for Robotics-SwxRWZaC1FM.zh-CN.vtt 891 B
Part 06-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.zh-CN.vtt 890 B
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/05. Confusion-Matrix-Solution-ywwSzyU9rYs.pt-BR.vtt 889 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.pt-BR.vtt 889 B
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Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.ja.vtt 887 B
Part 06-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.zh-CN.vtt 887 B
Part 09-Module 01-Lesson 01_Intro to Computer Vision/11. Emotion as a Service-2jAP3rP3USM.en.vtt 887 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.es-ES.vtt 886 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.ar.vtt 886 B
Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.ar.vtt 885 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.es-ES.vtt 885 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.pt-BR.vtt 884 B
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Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.en.vtt 883 B
Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.ar.vtt 882 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.es-ES.vtt 880 B
Part 01-Module 02-Lesson 02_Outliers and Filtering/01. M2L2 01 Intro V1-OGx1aYHMgbs.en.vtt 879 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.en.vtt 879 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.en.vtt 879 B
Part 01-Module 01-Lesson 05_Market Mechanics/11. M1L3 15 Outro V2-XVvfToYCsmo.en.vtt 878 B
Part 02-Module 02-Lesson 02_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.pt-BR.vtt 874 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.en.vtt 874 B
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/20. L1 23 Absolute Returns V3-wbb6WSyXLdU.en.vtt 872 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.ja.vtt 872 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.es-ES.vtt 870 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian - Artificial Intelligence for Robotics-fRYtUP0P4Lg.es-ES.vtt 869 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.en-GB.vtt 868 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.pt-BR.vtt 866 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.it.vtt 866 B
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/23. L1 26 Expense Ratios V2-SHZ0AhJq134.en.vtt 865 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.pt-BR.vtt 864 B
Part 03-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.zh-CN.vtt 862 B
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.ar.vtt 861 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/12. KALMAN QUIZ Predicting The Peak 01 RENDER V1-_fGH3xJMxdM.zh-CN.vtt 859 B
Part 01-Module 01-Lesson 07_Stock Returns/01. M1L5 01 Intro V2-mE8OOxkgzy8.en.vtt 858 B
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Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.en.vtt 856 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-4Fkfu37el_k.pt-BR.vtt 856 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-4Fkfu37el_k.en.vtt 855 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.hr.vtt 854 B
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Part 01-Module 01-Lesson 01_Welcome to the Nanodegree Program/02. M1L1 02 Interview W Jonathan V1-AeranuDRL7k.en.vtt 851 B
Part 06-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.ar.vtt 851 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.zh-CN.vtt 851 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.es-ES.vtt 850 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.zh-CN.vtt 848 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.pt-BR.vtt 848 B
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Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.ja.vtt 846 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian - Artificial Intelligence for Robotics-fRYtUP0P4Lg.en.vtt 846 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.ar.vtt 845 B
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.es-ES.vtt 845 B
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.ar.vtt 844 B
Part 01-Module 04-Lesson 01_Factors/10. M4 L1A 09 Overview For Standardizing A Factor V3-0clT0lnrTrU.en.vtt 842 B
Part 06-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.ja.vtt 841 B
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.ar.vtt 841 B
Part 02-Module 02-Lesson 02_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.zh-CN.vtt 840 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.en.vtt 839 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.ar.vtt 838 B
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.es-ES.vtt 837 B
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.it.vtt 837 B
Part 02-Module 03-Lesson 07_Feature Importance/09. M7L7 30 Shapley Solution V1-YmCSCA8Psgk.en.vtt 836 B
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.ar.vtt 836 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.ja.vtt 835 B
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.it.vtt 832 B
Part 07-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt 831 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.ar.vtt 829 B
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.en.vtt 828 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.zh-CN.vtt 828 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.ja.vtt 828 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.en.vtt 826 B
Part 01-Module 01-Lesson 06_Data Processing/01. M1L4 01 Stock Data V2-sN0_IqmMGGA.en.vtt 825 B
Part 02-Module 02-Lesson 02_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.en.vtt 824 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.es-ES.vtt 823 B
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.en.vtt 821 B
Part 02-Module 03-Lesson 07_Feature Importance/14. L7 Outro V1-Y2E-XN3lnWM.en.vtt 819 B
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.ar.vtt 819 B
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.ja.vtt 819 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function - Artificial Intelligence for Robotics-DV2cX9W0tT8.es-ES.vtt 819 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.zh-CN.vtt 818 B
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.en.vtt 818 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.zh-CN.vtt 817 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.es-ES.vtt 817 B
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/25. L1 28 Open End Mutual Funds Handling Withdrawals V2-46NGAQHY-Mc.en.vtt 816 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/13. KALMAN QUIZ Predicting The Peak 02 RENDER V1-mcwr6FcP2Vc.en.vtt 815 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.th.vtt 813 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.ja.vtt 813 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 B
Part 01-Module 03-Lesson 01_Stocks, Indices, Funds/08. L1 09 Price Weighting V2-2SFbwJ19NhA.en.vtt 812 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt 812 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-4Fkfu37el_k.zh-CN.vtt 812 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function - Artificial Intelligence for Robotics-DV2cX9W0tT8.ja-JP.vtt 812 B
Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.pt-BR.vtt 810 B
Part 09-Module 01-Lesson 01_Intro to Computer Vision/11. Emotion as a Service-2jAP3rP3USM.zh-CN.vtt 809 B
Part 03-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.en.vtt 806 B
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.ja.vtt 805 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.en-GB.vtt 804 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 B
Part 06-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.pt-BR.vtt 802 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.ar.vtt 802 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.en.vtt 799 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ar.vtt 799 B
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.en.vtt 798 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.it.vtt 797 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.pt-BR.vtt 797 B
Part 07-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt 793 B
Part 01-Module 03-Lesson 02_ETFs/11. L2 14 Summary V1-E5br2PiH8kY.en.vtt 792 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.hr.vtt 792 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.en.vtt 790 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.en.vtt 790 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 B
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.en.vtt 789 B
Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.ar.vtt 787 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.zh-CN.vtt 787 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.zh-CN.vtt 785 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.zh-CN.vtt 785 B
Part 01-Module 03-Lesson 04_Portfolio Optimization/13. L4 14 Recap V1-e3qJYCQfJD0.en.vtt 783 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.ar.vtt 783 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-lS5DfbsWH34.pt-BR.vtt 781 B
Part 06-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.pt-BR.vtt 780 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.th.vtt 779 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.ja-JP.vtt 777 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.th.vtt 777 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.es-ES.vtt 777 B
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.zh-CN.vtt 775 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.es-ES.vtt 775 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-R6oIvdBtsZw.pt-BR.vtt 773 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.th.vtt 772 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.en.vtt 772 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-lS5DfbsWH34.en.vtt 772 B
Part 02-Module 03-Lesson 02_Decision Trees/15. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt 771 B
Part 07-Module 01-Lesson 04_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt 771 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.pt-BR.vtt 769 B
Part 06-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.pt-BR.vtt 769 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.th.vtt 768 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-R6oIvdBtsZw.en.vtt 768 B
Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.it.vtt 767 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.ar.vtt 765 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pt-PT.vtt 765 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.it.vtt 764 B
Part 03-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.en.vtt 763 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.ja.vtt 762 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function - Artificial Intelligence for Robotics-DV2cX9W0tT8.en.vtt 762 B
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.pt-BR.vtt 761 B
Part 01-Module 03-Lesson 03_Portfolio Risk and Return/16. L3 13 Summary V1-I7XKJf8t_0s.en.vtt 760 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.ja.vtt 760 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.es-ES.vtt 757 B
Part 06-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.en.vtt 754 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.pt-BR.vtt 753 B
Part 06-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.zh-CN.vtt 752 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.en.vtt 752 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.ar.vtt 752 B
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.zh-CN.vtt 750 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.hr.vtt 748 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.es-ES.vtt 748 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.ja.vtt 748 B
Part 03-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.pt-BR.vtt 746 B
Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.es-ES.vtt 746 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.es-ES.vtt 746 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian - Artificial Intelligence for Robotics-fRYtUP0P4Lg.zh-CN.vtt 746 B
Part 03-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.zh-CN.vtt 745 B
Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.pt-BR.vtt 745 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-R6oIvdBtsZw.zh-CN.vtt 744 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.it.vtt 742 B
Part 06-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.zh-CN.vtt 742 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.pt-BR.vtt 741 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.es-ES.vtt 741 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.ar.vtt 738 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.en.vtt 738 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.pt-BR.vtt 736 B
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.pt-BR.vtt 735 B
Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.pt-BR.vtt 735 B
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/07. Remember Gate-0qlm86HaXuU.en.vtt 734 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.en.vtt 734 B
Part 06-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.ar.vtt 731 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.ja.vtt 730 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.th.vtt 729 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.es-ES.vtt 729 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.pt-BR.vtt 728 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.es-ES.vtt 728 B
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/03. Grammar-Jw3dA7xmoQ4.en.vtt 727 B
Part 02-Module 03-Lesson 02_Decision Trees/15. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt 727 B
Part 07-Module 01-Lesson 04_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt 727 B
Part 10-Module 01-Lesson 01_Intro to NLP/04. Grammar-Jw3dA7xmoQ4.en.vtt 727 B
Part 03-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.zh-CN.vtt 724 B
Part 02-Module 03-Lesson 02_Decision Trees/15. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt 723 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.th.vtt 723 B
Part 07-Module 01-Lesson 04_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt 723 B
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/07. Accuracy 2-ueYCLfd_aNQ.en-US.vtt 720 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.ar.vtt 717 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.ja.vtt 717 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.th.vtt 717 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function - Artificial Intelligence for Robotics-DV2cX9W0tT8.zh-CN.vtt 717 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.ja.vtt 716 B
Part 07-Module 01-Lesson 02_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt 716 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.en.vtt 715 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.en.vtt 715 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pl.vtt 715 B
Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.en.vtt 714 B
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.zh-CN.vtt 713 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.ja-JP.vtt 712 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.it.vtt 712 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.ja-JP.vtt 712 B
Part 01-Module 02-Lesson 03_Regression/17. M2L3 15 Summary V1-n2VxcEcw0GY.en.vtt 710 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.ja.vtt 710 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.ja.vtt 708 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.zh-CN.vtt 708 B
Part 06-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.zh-CN.vtt 707 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ar.vtt 706 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt 705 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.pt-BR.vtt 705 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.en.vtt 704 B
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.zh-CN.vtt 704 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.it.vtt 702 B
Part 06-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.pt-BR.vtt 702 B
Part 01-Module 04-Lesson 05_Risk Factor Models with PCA/19. M4 L2b 19 Outro V1-nfVnAkndJCY.en.vtt 700 B
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/07. Remember Gate-0qlm86HaXuU.pt-BR.vtt 700 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.th.vtt 700 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.pt-BR.vtt 699 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.ja-JP.vtt 698 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.ja-JP.vtt 698 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.it.vtt 697 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/20. Predict Function - Artificial Intelligence for Robotics-DV2cX9W0tT8.ja.vtt 697 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.it.vtt 695 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.pt-BR.vtt 695 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.zh-CN.vtt 695 B
Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.pt-BR.vtt 695 B
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.pt-BR.vtt 694 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.pt-BR.vtt 692 B
Part 06-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.pt-BR.vtt 692 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.pt-BR.vtt 691 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.hr.vtt 690 B
Part 06-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.en.vtt 690 B
Part 07-Module 01-Lesson 02_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt 690 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.zh-CN.vtt 689 B
Part 02-Module 01-Lesson 04_Feature Extraction/10. NLP Summary-B9ul8fsQYOA.en.vtt 688 B
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/07. Accuracy 2-ueYCLfd_aNQ.en.vtt 688 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.pt-BR.vtt 688 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.pt-BR.vtt 687 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.es-ES.vtt 686 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/13. KALMAN QUIZ Predicting The Peak 02 RENDER V1-mcwr6FcP2Vc.zh-CN.vtt 686 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.ar.vtt 684 B
Part 06-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.ar.vtt 684 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.ar.vtt 684 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pt-BR.vtt 684 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.en.vtt 682 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.es-ES.vtt 682 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.en.vtt 682 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.ar.vtt 682 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.es-ES.vtt 681 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.pt-BR.vtt 680 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-lS5DfbsWH34.zh-CN.vtt 680 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/15. KALMAN QUIZ Parameter Update 02 RENDER V2-vl6GkkEgY4M.en.vtt 680 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.ar.vtt 679 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.es-ES.vtt 679 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.es-ES.vtt 679 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.th.vtt 677 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.zh-CN.vtt 677 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.zh-CN.vtt 676 B
Part 06-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.en.vtt 675 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.zh-CN.vtt 675 B
Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.ar.vtt 673 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.zh-CN.vtt 673 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.pt-BR.vtt 673 B
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt 672 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.es-ES.vtt 671 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.it.vtt 671 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.es-ES.vtt 671 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.pt-BR.vtt 671 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.ar.vtt 670 B
Part 06-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.zh-CN.vtt 670 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.ja.vtt 669 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.th.vtt 667 B
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.es-ES.vtt 665 B
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.en.vtt 665 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.ar.vtt 663 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.en.vtt 663 B
Part 01-Module 03-Lesson 04_Portfolio Optimization/01. L4 01 Intro V1-CtIcmmR0YTs.en.vtt 662 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.hr.vtt 662 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.ja.vtt 661 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.zh-CN.vtt 659 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.zh-CN.vtt 658 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.th.vtt 657 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.ja.vtt 657 B
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/07. Accuracy 2-ueYCLfd_aNQ.pt.vtt 656 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.es-ES.vtt 656 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.it.vtt 656 B
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.ar.vtt 656 B
Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.en.vtt 655 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.en.vtt 655 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.zh-CN.vtt 655 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.th.vtt 654 B
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.ar.vtt 654 B
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.en.vtt 653 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.th.vtt 653 B
Part 03-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.ar.vtt 652 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.ar.vtt 650 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.ar.vtt 650 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.en.vtt 646 B
Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.en.vtt 645 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.pt-BR.vtt 642 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.hr.vtt 640 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.ar.vtt 640 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.zh-CN.vtt 638 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.hr.vtt 637 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.en.vtt 635 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.zh-CN.vtt 635 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.th.vtt 634 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 B
Part 06-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.zh-CN.vtt 633 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.en.vtt 633 B
Part 07-Module 01-Lesson 03_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt 633 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 B
Part 02-Module 02-Lesson 04_Recurrent Neural Networks/07. Remember Gate-0qlm86HaXuU.zh-CN.vtt 632 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/11. KALMAN QUIZ Shifting The Mean 02 RENDER V1-L8vNIKvpJ1s.en.vtt 632 B
Part 07-Module 01-Lesson 02_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt 631 B
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.pt-BR.vtt 629 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.zh-CN.vtt 629 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.es-ES.vtt 629 B
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.en.vtt 628 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.ja.vtt 627 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.ar.vtt 626 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.ja.vtt 625 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.it.vtt 625 B
Part 02-Module 01-Lesson 04_Feature Extraction/10. NLP Summary-B9ul8fsQYOA.zh-CN.vtt 624 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.ja.vtt 623 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.ar.vtt 623 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.en.vtt 622 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.zh-CN.vtt 622 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.ja.vtt 621 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.pt-BR.vtt 620 B
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/07. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt 618 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.it.vtt 618 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.ja.vtt 618 B
Part 02-Module 01-Lesson 02_Intro to Natural Language Processing/03. Grammar-Jw3dA7xmoQ4.zh-CN.vtt 617 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ja.vtt 617 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ru.vtt 617 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.en.vtt 617 B
Part 06-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.zh-CN.vtt 617 B
Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.en.vtt 617 B
Part 10-Module 01-Lesson 01_Intro to NLP/04. Grammar-Jw3dA7xmoQ4.zh-CN.vtt 617 B
Part 06-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.zh-CN.vtt 616 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.zh-CN.vtt 616 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.ar.vtt 615 B
Part 06-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.zh-CN.vtt 613 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.en.vtt 613 B
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 44 Weight Intro V1-1cautGeQWDE.en.vtt 612 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.es-ES.vtt 611 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.ja-JP.vtt 610 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.zh-CN.vtt 609 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.es-ES.vtt 608 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.ja.vtt 608 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.ar.vtt 607 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.zh-CN.vtt 607 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 B
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt 606 B
Part 02-Module 03-Lesson 06_Overlapping Labels/06. L6 08 HS Ensemble Models Trained On Nonoverlapping Periods V5-YiXkL-Ts67I.en.vtt 604 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.th.vtt 604 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.th.vtt 603 B
Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt 601 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.ja-JP.vtt 599 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.hr.vtt 599 B
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.pt-BR.vtt 599 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.ar.vtt 599 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.en.vtt 599 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.it.vtt 598 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.pt-BR.vtt 598 B
Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.en.vtt 598 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.ja.vtt 597 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.pt-BR.vtt 597 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.ja.vtt 596 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.es-ES.vtt 595 B
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt 595 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.ja.vtt 594 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.pt-BR.vtt 593 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.ar.vtt 591 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.en.vtt 591 B
Part 07-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt 590 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.pt-BR.vtt 589 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.ja.vtt 589 B
Part 07-Module 01-Lesson 03_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt 589 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.ja.vtt 587 B
Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.en.vtt 586 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.ja.vtt 584 B
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.ar.vtt 584 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 B
Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.pt-BR.vtt 583 B
Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt 583 B
Part 06-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.zh-CN.vtt 582 B
Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.pt-BR.vtt 582 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-Hans.vtt 579 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.th.vtt 579 B
Part 06-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.pt-BR.vtt 579 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.th.vtt 578 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.zh-CN.vtt 577 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.hr.vtt 575 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.es-ES.vtt 575 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.zh-CN.vtt 574 B
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.ja.vtt 572 B
Part 06-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.zh-CN.vtt 572 B
Part 06-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.en.vtt 572 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.it.vtt 571 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.en.vtt 571 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.pt-BR.vtt 570 B
Part 03-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.zh-CN.vtt 569 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.en.vtt 569 B
Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.pt-BR.vtt 567 B
Part 06-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.zh-CN.vtt 566 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.es-ES.vtt 566 B
Part 01-Module 02-Lesson 02_Outliers and Filtering/10. M2L2 09 Outro V1-r1SWu-7Rzf0.en.vtt 565 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.hr.vtt 565 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.es-ES.vtt 565 B
Part 02-Module 01-Lesson 06_Basic NLP Analysis/09. AIT M5L5 99 Summary V1-ThLOv6gDyHI.en.vtt 563 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ar.vtt 562 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.ja-JP.vtt 561 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.ja-JP.vtt 561 B
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.es-ES.vtt 560 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.zh-CN.vtt 558 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.en.vtt 558 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.ar.vtt 558 B
Part 07-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt 558 B
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.ja.vtt 557 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-CN.vtt 556 B
Part 06-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.zh-CN.vtt 554 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.th.vtt 554 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.en.vtt 553 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.zh-CN.vtt 553 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.it.vtt 552 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.pt-BR.vtt 552 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.ja-JP.vtt 551 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.ja-JP.vtt 551 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ar.vtt 549 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.en.vtt 549 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.th.vtt 548 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.ar.vtt 547 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.pt-BR.vtt 547 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.pt-BR.vtt 547 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ar.vtt 544 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.th.vtt 541 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.ja.vtt 541 B
Part 06-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.pt-BR.vtt 541 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.en.vtt 540 B
Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt 540 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.hr.vtt 539 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.zh-CN.vtt 538 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.es-ES.vtt 538 B
Part 06-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.zh-CN.vtt 537 B
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.en.vtt 537 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.es-ES.vtt 535 B
Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.ar.vtt 534 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ar.vtt 533 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.es-ES.vtt 533 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/11. KALMAN QUIZ Shifting The Mean 02 RENDER V1-L8vNIKvpJ1s.zh-CN.vtt 533 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.ja.vtt 532 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.ar.vtt 532 B
Part 06-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.zh-CN.vtt 532 B
Part 06-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.zh-CN.vtt 532 B
Part 01-Module 02-Lesson 05_Volatility/14. M2L5 15 Outro V1-FMXL37CkTgg.en.vtt 531 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ja.vtt 531 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.es-ES.vtt 531 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.hr.vtt 530 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.pt-BR.vtt 530 B
Part 06-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.en.vtt 530 B
Part 07-Module 01-Lesson 03_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt 530 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.es-ES.vtt 529 B
Part 02-Module 03-Lesson 03_Model Testing and Evaluation/07. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt 528 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.en.vtt 528 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.ja.vtt 527 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.ja.vtt 525 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ar.vtt 524 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.en.vtt 524 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.en.vtt 523 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.pt-BR.vtt 523 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.pt-BR.vtt 523 B
Part 06-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.zh-CN.vtt 521 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt 521 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.zh-CN.vtt 519 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ar.vtt 518 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.ar.vtt 518 B
Part 06-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.hr.vtt 517 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/15. KALMAN QUIZ Parameter Update 02 RENDER V2-vl6GkkEgY4M.zh-CN.vtt 517 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.zh-CN.vtt 516 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.pt-BR.vtt 516 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ar.vtt 516 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.es-ES.vtt 516 B
Part 02-Module 05-Lesson 01_Intro to Backtesting/11. M8L1 15 Outro V1-mFk_HYJLF1w.en.vtt 510 B
Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-9J3IwQFXveI.ar.vtt 510 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.ar.vtt 509 B
Part 03-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.pt-BR.vtt 508 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.en.vtt 508 B
Part 06-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.zh-CN.vtt 507 B
Part 02-Module 03-Lesson 07_Feature Importance/11. M7L7 45 Weight Solution V1-VTQJc3Q7m9M.en.vtt 504 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.it.vtt 504 B
Part 06-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.zh-CN.vtt 504 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.pt-BR.vtt 503 B
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.es-ES.vtt 502 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.en.vtt 501 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.pt-BR.vtt 501 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 B
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.pt-BR.vtt 500 B
Part 06-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.zh-CN.vtt 499 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.pt-BR.vtt 498 B
Part 06-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.zh-CN.vtt 497 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.pt-BR.vtt 497 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.ar.vtt 497 B
Part 02-Module 01-Lesson 05_Financial Statements/26. AIT M5L4B 99 Summary V1-NgIufQFEHps.en.vtt 496 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.pt-BR.vtt 496 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.en.vtt 496 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 B
Part 06-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.en.vtt 495 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.pt-BR.vtt 495 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.ar.vtt 493 B
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.es-ES.vtt 493 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.ja-JP.vtt 492 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.zh-CN.vtt 492 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.ja.vtt 492 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.ar.vtt 491 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.ja.vtt 491 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt 490 B
Part 07-Module 01-Lesson 03_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt 488 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.en.vtt 486 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.ja-JP.vtt 484 B
Part 06-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.en.vtt 484 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.ja.vtt 483 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ar.vtt 481 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.pt-BR.vtt 481 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.es-ES.vtt 480 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.th.vtt 480 B
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.en.vtt 479 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.ar.vtt 479 B
Part 02-Module 02-Lesson 02_Training Neural Networks/10. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 B
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.es-ES.vtt 478 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.ja-JP.vtt 477 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.ja-JP.vtt 477 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.en.vtt 476 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.it.vtt 476 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.ja-JP.vtt 473 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt 473 B
Part 03-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.en.vtt 473 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.es-ES.vtt 473 B
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.ja.vtt 473 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.pt-BR.vtt 473 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.ar.vtt 473 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.ar.vtt 473 B
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.pt-BR.vtt 473 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.ar.vtt 472 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.es-ES.vtt 471 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.es-ES.vtt 471 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.en.vtt 470 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.hr.vtt 469 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.en.vtt 468 B
Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.en.vtt 467 B
Part 02-Module 02-Lesson 02_Training Neural Networks/10. Random Restart-idyBBCzXiqg.en.vtt 466 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.es-ES.vtt 466 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.pt-BR.vtt 465 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.ar.vtt 463 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.en.vtt 461 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.zh-CN.vtt 460 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.ar.vtt 459 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.es-ES.vtt 459 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ja.vtt 458 B
Part 06-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.zh-CN.vtt 458 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.en.vtt 458 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt 457 B
Part 03-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.zh-CN.vtt 456 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.it.vtt 456 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.ar.vtt 456 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.es-ES.vtt 455 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.en.vtt 455 B
README.txt 454 B
Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt 454 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.hr.vtt 453 B
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.en.vtt 453 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.it.vtt 452 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.es-ES.vtt 451 B
Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt 451 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.ja.vtt 449 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.pt-BR.vtt 447 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.th.vtt 447 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ar.vtt 445 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.it.vtt 444 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.ja.vtt 444 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.pt-BR.vtt 444 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-uC1Xwc7warg.ar.vtt 444 B
Part 06-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.zh-CN.vtt 442 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.pt-BR.vtt 442 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.th.vtt 441 B
Part 06-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.zh-CN.vtt 441 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.zh-CN.vtt 441 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.pt-BR.vtt 440 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.zh-CN.vtt 439 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt 439 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ar.vtt 437 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ar.vtt 436 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.it.vtt 435 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.en.vtt 433 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.it.vtt 433 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.pt-BR.vtt 433 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.en.vtt 432 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.zh-CN.vtt 431 B
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.en.vtt 428 B
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.ja.vtt 428 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.pt-BR.vtt 427 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.en.vtt 427 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.es-ES.vtt 426 B
Part 01-Module 02-Lesson 04_Time Series Modeling/09. M2L4 11 Outro V1-6sheR92KUU8.en.vtt 425 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.hr.vtt 425 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.en.vtt 425 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.es-ES.vtt 425 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.es-ES.vtt 424 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.en.vtt 424 B
Part 07-Module 01-Lesson 03_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.zh-CN.vtt 424 B
Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.en.vtt 423 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.th.vtt 423 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.zh-CN.vtt 423 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.it.vtt 422 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.pt-BR.vtt 422 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ar.vtt 420 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.ja.vtt 420 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.zh-CN.vtt 420 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 B
Part 02-Module 02-Lesson 02_Training Neural Networks/10. Random Restart-idyBBCzXiqg.zh-CN.vtt 419 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.ja.vtt 419 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.pt-BR.vtt 418 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.es-ES.vtt 417 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.ja.vtt 417 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.en.vtt 416 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.ja.vtt 415 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.th.vtt 415 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.it.vtt 414 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.en.vtt 414 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.es-ES.vtt 412 B
Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.pt-BR.vtt 411 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.ar.vtt 411 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 B
Part 03-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.zh-CN.vtt 410 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.pt-BR.vtt 410 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.pt-BR.vtt 410 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.it.vtt 409 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.zh-CN.vtt 408 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.es-ES.vtt 408 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.es-ES.vtt 406 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ar.vtt 405 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.ar.vtt 404 B
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.ja.vtt 403 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.zh-CN.vtt 403 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.pt-BR.vtt 401 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ar.vtt 399 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.en.vtt 399 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.ar.vtt 399 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.th.vtt 398 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.ja.vtt 397 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.pt-BR.vtt 397 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.en.vtt 397 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ar.vtt 396 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.pt-BR.vtt 396 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.ar.vtt 395 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ja.vtt 395 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.es-ES.vtt 393 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.pt-BR.vtt 393 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.th.vtt 393 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.pt-BR.vtt 391 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ja.vtt 390 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.es-ES.vtt 390 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.zh-CN.vtt 390 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 B
Part 03-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.zh-CN.vtt 389 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.zh-CN.vtt 389 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.ja.vtt 389 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.ja.vtt 389 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.es-ES.vtt 389 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.es-ES.vtt 388 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.en.vtt 387 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.zh-CN.vtt 385 B
Part 07-Module 01-Lesson 03_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt 385 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt 385 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.hr.vtt 384 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.hr.vtt 383 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.pt-BR.vtt 383 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.it.vtt 382 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.zh-CN.vtt 380 B
Part 06-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.zh-CN.vtt 380 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.zh-CN.vtt 379 B
Part 02-Module 05-Lesson 03_Attribution/01. M8L4 01 Intro V1-sIh09ScQXCQ.en.vtt 378 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.pt-BR.vtt 377 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.pt-BR.vtt 377 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.zh-CN.vtt 376 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.ja-JP.vtt 376 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.it.vtt 375 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.pt-BR.vtt 375 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ja.vtt 374 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.en.vtt 373 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.ja.vtt 373 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.pt-BR.vtt 373 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.it.vtt 372 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ja.vtt 371 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ja.vtt 370 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.en.vtt 370 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt 370 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.zh-CN.vtt 369 B
Part 06-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.zh-CN.vtt 369 B
Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt 369 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.en.vtt 368 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.es-ES.vtt 367 B
Part 02-Module 05-Lesson 03_Attribution/09. M8L4 010 Outro V1-PNnSfMm-7-s.en.vtt 366 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.es-ES.vtt 366 B
Part 02-Module 02-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ar.vtt 364 B
Part 06-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.zh-CN.vtt 364 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.en.vtt 364 B
Part 08-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.pt-BR.vtt 362 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt 361 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.pt-BR.vtt 360 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.es-ES.vtt 360 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.th.vtt 359 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.it.vtt 359 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.es-ES.vtt 358 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.en.vtt 358 B
Part 02-Module 04-Lesson 02_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ja.vtt 357 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.es-ES.vtt 357 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.en.vtt 357 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.es-ES.vtt 357 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.es-ES.vtt 356 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.pt-BR.vtt 356 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.zh-CN.vtt 355 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.es-ES.vtt 355 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.zh-CN.vtt 355 B
Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt 355 B
Part 07-Module 01-Lesson 03_Clustering/08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt 355 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.th.vtt 354 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.pt-BR.vtt 354 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.en.vtt 354 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.ja.vtt 353 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.en.vtt 353 B
Part 06-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.zh-CN.vtt 351 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.en.vtt 351 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.es-ES.vtt 350 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ja.vtt 350 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.pt-BR.vtt 349 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.en.vtt 349 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.en.vtt 349 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.zh-CN.vtt 349 B
Part 06-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.zh-CN.vtt 348 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.es-ES.vtt 348 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.es-ES.vtt 348 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.ja.vtt 345 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.en.vtt 343 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.th.vtt 342 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.en.vtt 342 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.ar.vtt 342 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.en.vtt 342 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.en.vtt 341 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.zh-CN.vtt 341 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.it.vtt 341 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.ar.vtt 341 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.en.vtt 340 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.ja.vtt 340 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.en.vtt 339 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.ar.vtt 339 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.pt-BR.vtt 339 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.th.vtt 339 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.it.vtt 338 B
Part 06-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.es-ES.vtt 337 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ar.vtt 336 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.en.vtt 336 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.pt-BR.vtt 335 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.ar.vtt 334 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.es-ES.vtt 333 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.es-ES.vtt 332 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ja.vtt 332 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.pt-BR.vtt 332 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt 332 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ja.vtt 331 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.zh-CN.vtt 331 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.es-ES.vtt 329 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.pt-BR.vtt 329 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.zh-CN.vtt 328 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.es-ES.vtt 327 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.ja.vtt 327 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.ja.vtt 326 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.ja.vtt 326 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt 326 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.en.vtt 325 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ar.vtt 325 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.it.vtt 324 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.zh-CN.vtt 323 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.ja.vtt 323 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.en.vtt 322 B
Part 06-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.zh-CN.vtt 321 B
Part 06-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.zh-CN.vtt 320 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.hr.vtt 319 B
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.ar.vtt 319 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/09. Maximize Gaussian Solution - Artificial Intelligence for Robotics-2cD8T65E-jM.ja.vtt 319 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.es-ES.vtt 318 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.en.vtt 317 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.pt-BR.vtt 317 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.ja-JP.vtt 317 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.zh-CN.vtt 316 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.it.vtt 315 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ja.vtt 315 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ar.vtt 315 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt 315 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.it.vtt 314 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.zh-CN.vtt 314 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.es-ES.vtt 314 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.en.vtt 313 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.ar.vtt 312 B
Part 06-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.hr.vtt 312 B
Part 07-Module 01-Lesson 03_Clustering/07. Moving Centers 2-uC1Xwc7warg.en.vtt 312 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.it.vtt 311 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.zh-CN.vtt 311 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.en.vtt 310 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.ja.vtt 310 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.it.vtt 308 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.it.vtt 307 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.en.vtt 307 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt 306 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ja.vtt 305 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.hr.vtt 305 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ja.vtt 305 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.th.vtt 305 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.ja.vtt 304 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.zh-CN.vtt 304 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.ja.vtt 304 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.ar.vtt 303 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.ar.vtt 303 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.zh-CN.vtt 303 B
Part 06-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.zh-CN.vtt 303 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.ar.vtt 302 B
Part 06-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.zh-CN.vtt 302 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.ar.vtt 302 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.ja.vtt 302 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.ja.vtt 301 B
Part 06-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ja.vtt 301 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.es-ES.vtt 301 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.ja.vtt 300 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.ja.vtt 300 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.th.vtt 299 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.ja.vtt 298 B
Part 06-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.zh-CN.vtt 298 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.zh-CN.vtt 297 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.en.vtt 297 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ja.vtt 296 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ja.vtt 293 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.zh-CN.vtt 293 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.pt-BR.vtt 293 B
Part 07-Module 01-Lesson 05_Introduction to Kalman Filters/22. Predict Function Solution - Artificial Intelligence for Robotics-AMFig-sYGfM.zh-CN.vtt 292 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.hr.vtt 291 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.it.vtt 291 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.pt-BR.vtt 291 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.zh-CN.vtt 290 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.hr.vtt 289 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.it.vtt 289 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.ar.vtt 286 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.pt-BR.vtt 285 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.pt-BR.vtt 284 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.ar.vtt 283 B
Part 06-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.zh-CN.vtt 282 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.en.vtt 281 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.es-ES.vtt 281 B
Part 06-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.zh-CN.vtt 281 B
Part 06-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.zh-CN.vtt 280 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.ar.vtt 279 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.en.vtt 279 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.ar.vtt 278 B
Part 06-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.zh-CN.vtt 277 B
Part 07-Module 01-Lesson 03_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt 277 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.pt-BR.vtt 276 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.zh-CN.vtt 275 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.es-ES.vtt 272 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.zh-CN.vtt 272 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.pt-BR.vtt 269 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.en.vtt 268 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.th.vtt 267 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.ar.vtt 265 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.pt-BR.vtt 265 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.ar.vtt 265 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.ja.vtt 264 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ja.vtt 262 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.pt-BR.vtt 262 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.ja.vtt 261 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.en.vtt 260 B
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.pt-BR.vtt 260 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.ja.vtt 260 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.es-ES.vtt 260 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.es-ES.vtt 258 B
Part 07-Module 01-Lesson 03_Clustering/09. Handoff to Katie-knrPsGtpyQY.ar.vtt 258 B
Part 06-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.zh-CN.vtt 257 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.es-ES.vtt 257 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.en.vtt 256 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.en.vtt 256 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.ar.vtt 254 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ja.vtt 254 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.pt-BR.vtt 254 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.pt-BR.vtt 253 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.hr.vtt 252 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.th.vtt 252 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.es-ES.vtt 252 B
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.ar.vtt 252 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.es-ES.vtt 251 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.ar.vtt 250 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.pt-BR.vtt 249 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.pt-BR.vtt 249 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.pt-BR.vtt 249 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.pt-BR.vtt 249 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.zh-CN.vtt 248 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.it.vtt 248 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.ja.vtt 248 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.en.vtt 248 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.ja.vtt 247 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.en.vtt 247 B
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.ja.vtt 247 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.en.vtt 246 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.pt-BR.vtt 246 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.en.vtt 245 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.en.vtt 244 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.zh-CN.vtt 244 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.th.vtt 244 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.ja.vtt 243 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.es-ES.vtt 243 B
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.es-ES.vtt 242 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.zh-CN.vtt 241 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.en.vtt 241 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.ja.vtt 241 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.en.vtt 240 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.it.vtt 240 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.th.vtt 240 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.th.vtt 240 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.pt-BR.vtt 240 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.it.vtt 239 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.es-ES.vtt 239 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.zh-CN.vtt 239 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.hr.vtt 238 B
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.en.vtt 238 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.es-ES.vtt 238 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.ar.vtt 238 B
Part 06-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.ja.vtt 237 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.ja.vtt 236 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.es-ES.vtt 236 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.es-ES.vtt 234 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.it.vtt 234 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.es-ES.vtt 234 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.es-ES.vtt 233 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.zh-CN.vtt 232 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.en.vtt 231 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.it.vtt 231 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.es-ES.vtt 231 B
Part 06-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.zh-CN.vtt 228 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.zh-CN.vtt 228 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.th.vtt 228 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.es-ES.vtt 227 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.zh-CN.vtt 227 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.zh-CN.vtt 226 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.pt-BR.vtt 223 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.pt-BR.vtt 223 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.ar.vtt 222 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.ar.vtt 222 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.en.vtt 222 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.ar.vtt 222 B
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.es-ES.vtt 221 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.ja.vtt 219 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.pt-BR.vtt 219 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.en.vtt 219 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.es-ES.vtt 219 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.en.vtt 219 B
Part 06-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.pt-BR.vtt 218 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.zh-CN.vtt 218 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.en.vtt 217 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.ja.vtt 217 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.es-ES.vtt 217 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.hr.vtt 216 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.ja.vtt 216 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.pt-BR.vtt 216 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.en.vtt 216 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.pt-PT.vtt 215 B
Part 06-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.zh-CN.vtt 215 B
Part 06-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.zh-CN.vtt 215 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.en.vtt 214 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.en.vtt 214 B
Part 06-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.zh-CN.vtt 214 B
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.en.vtt 214 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.hr.vtt 213 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.it.vtt 213 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.it.vtt 212 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.ja.vtt 212 B
Part 06-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.zh-CN.vtt 212 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.pt-BR.vtt 212 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.ar.vtt 211 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.es-ES.vtt 210 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.en.vtt 210 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.it.vtt 210 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.hr.vtt 209 B
Part 06-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.pt-BR.vtt 209 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.ar.vtt 208 B
Part 06-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.zh-CN.vtt 208 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.ar.vtt 207 B
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.zh-CN.vtt 207 B
Part 07-Module 01-Lesson 03_Clustering/09. Handoff to Katie-knrPsGtpyQY.en.vtt 207 B
Part 07-Module 01-Lesson 03_Clustering/09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt 206 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.it.vtt 205 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.th.vtt 205 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.ar.vtt 204 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.hr.vtt 204 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.th.vtt 204 B
Part 07-Module 01-Lesson 03_Clustering/09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt 204 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.ar.vtt 202 B
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.pt-BR.vtt 202 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.ja.vtt 202 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.ja.vtt 201 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.pt-BR.vtt 201 B
Part 06-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ja.vtt 200 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.ja.vtt 199 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.en.vtt 198 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.zh-CN.vtt 198 B
Part 06-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.ja.vtt 198 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.zh-CN.vtt 197 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.hr.vtt 197 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.ja.vtt 197 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.en.vtt 196 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.zh-CN.vtt 196 B
Part 06-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.zh-CN.vtt 195 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.ja.vtt 194 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.ja.vtt 194 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.es-ES.vtt 192 B
Part 06-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.zh-CN.vtt 192 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.ar.vtt 191 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.en.vtt 190 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.ja.vtt 190 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.ar.vtt 190 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.zh-Hans.vtt 189 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.it.vtt 187 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.ja.vtt 187 B
Part 06-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.pt-BR.vtt 186 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.es-ES.vtt 185 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.ar.vtt 184 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.pt-BR.vtt 184 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.ja.vtt 184 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.zh-CN.vtt 184 B
Part 06-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.zh-CN.vtt 183 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.pt-BR.vtt 183 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.zh-CN.vtt 180 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.en.vtt 180 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.ja.vtt 180 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.it.vtt 178 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.th.vtt 178 B
Part 06-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.zh-CN.vtt 177 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.th.vtt 177 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.ar.vtt 176 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.ja.vtt 176 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.ar.vtt 175 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.ja.vtt 175 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.es-ES.vtt 174 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.it.vtt 174 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.es-ES.vtt 174 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.it.vtt 173 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.pt-BR.vtt 173 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.pt-BR.vtt 173 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.it.vtt 171 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.it.vtt 171 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.pt-BR.vtt 171 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.zh-CN.vtt 170 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.es-ES.vtt 170 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.pt-BR.vtt 169 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.en.vtt 169 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.pt-BR.vtt 169 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.pt-BR.vtt 168 B
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.ar.vtt 167 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.en.vtt 167 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.ar.vtt 166 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.es-ES.vtt 166 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.en.vtt 165 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.es-ES.vtt 165 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.zh-CN.vtt 164 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.th.vtt 164 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.it.vtt 164 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.pt-BR.vtt 164 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.pt-BR.vtt 164 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.hr.vtt 163 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.zh-CN.vtt 163 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.ar.vtt 163 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.pt-BR.vtt 162 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.ar.vtt 161 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.es-ES.vtt 160 B
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.pt-BR.vtt 160 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.th.vtt 160 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.ja.vtt 159 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.pt-BR.vtt 158 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.zh-CN.vtt 158 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.ar.vtt 158 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.zh-CN.vtt 158 B
Part 06-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.hr.vtt 157 B
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.ja.vtt 157 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.es-ES.vtt 157 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.pt-BR.vtt 157 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.ja.vtt 156 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.en.vtt 155 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.zh-CN.vtt 155 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.hr.vtt 155 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.ja.vtt 155 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.es-ES.vtt 155 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.en.vtt 155 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.ja.vtt 154 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.ar.vtt 153 B
Part 06-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.zh-CN.vtt 152 B
Part 06-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.en.vtt 152 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.th.vtt 152 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.ar.vtt 152 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.it.vtt 151 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.zh-CN.vtt 151 B
Part 06-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.zh-CN.vtt 150 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.pt-BR.vtt 149 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.hr.vtt 149 B
Part 06-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.ja.vtt 149 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.pt-BR.vtt 149 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.zh-CN.vtt 149 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.zh-CN.vtt 149 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.en.vtt 148 B
Part 06-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.en.vtt 147 B
Part 06-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.en.vtt 146 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.pt-BR.vtt 146 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.zh-CN.vtt 146 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.hr.vtt 144 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.ar.vtt 144 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.es-ES.vtt 143 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.es-ES.vtt 142 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.ja.vtt 142 B
Part 06-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.en.vtt 142 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.es-ES.vtt 141 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.ar.vtt 141 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.ja.vtt 141 B
Part 06-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.ja.vtt 141 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.es-ES.vtt 141 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.en.vtt 140 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.es-ES.vtt 140 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.ja.vtt 139 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.en.vtt 138 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.it.vtt 138 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.ja.vtt 138 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.es-ES.vtt 138 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.ja.vtt 137 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.th.vtt 137 B
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.zh-CN.vtt 136 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.en.vtt 136 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.th.vtt 135 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.ja.vtt 134 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.ar.vtt 132 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.ja.vtt 132 B
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.es-ES.vtt 130 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.th.vtt 130 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.en.vtt 130 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.it.vtt 129 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.en.vtt 129 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.zh-CN.vtt 129 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.ar.vtt 129 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.es-ES.vtt 129 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.en.vtt 128 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.it.vtt 127 B
Part 06-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.zh-CN.vtt 127 B
Part 06-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.zh-CN.vtt 126 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.ja.vtt 126 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.ar.vtt 126 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.es-ES.vtt 125 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.es-ES.vtt 125 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.pt-BR.vtt 125 B
Part 06-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.zh-CN.vtt 123 B
Part 06-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.zh-CN.vtt 123 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.pt-BR.vtt 123 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.ja.vtt 123 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.ja.vtt 123 B
Part 06-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.en.vtt 122 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.ja.vtt 122 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.ar.vtt 122 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.pt-BR.vtt 122 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.en.vtt 120 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.es-ES.vtt 120 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-Hans.vtt 119 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.en.vtt 119 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.en.vtt 118 B
Part 06-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.zh-CN.vtt 118 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.ar.vtt 118 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.es-ES.vtt 118 B
Part 06-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.ja.vtt 118 B
Part 06-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.zh-CN.vtt 117 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ja.vtt 116 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.zh-CN.vtt 116 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-CN.vtt 115 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.zh-CN.vtt 113 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.ja.vtt 113 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.zh-CN.vtt 111 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.ar.vtt 110 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.ja.vtt 110 B
Part 06-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.en.vtt 110 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.zh-CN.vtt 110 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.es-ES.vtt 109 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.es-ES.vtt 109 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.en.vtt 109 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ar.vtt 108 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.th.vtt 108 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.pt-BR.vtt 108 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.es-ES.vtt 108 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.pt-BR.vtt 108 B
Part 06-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.zh-CN.vtt 108 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.es-ES.vtt 107 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.pt-BR.vtt 106 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.es-ES.vtt 104 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.en.vtt 104 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.en.vtt 103 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.pt-BR.vtt 101 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.zh-Hans.vtt 101 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.es-ES.vtt 101 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.ja.vtt 100 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.pt-BR.vtt 100 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.ar.vtt 99 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.pt-BR.vtt 99 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.zh-CN.vtt 99 B
Part 06-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.pt-BR.vtt 99 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.zh-Hans.vtt 98 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.ar.vtt 98 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.zh-CN.vtt 98 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.ja.vtt 97 B
Part 06-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.en.vtt 97 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.it.vtt 96 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.ar.vtt 96 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.es-ES.vtt 95 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.es-ES.vtt 95 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.en.vtt 95 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.hr.vtt 95 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.ar.vtt 95 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.en.vtt 95 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.zh-CN.vtt 94 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.ar.vtt 94 B
Part 06-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.it.vtt 94 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.ja.vtt 94 B
Part 06-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.pt-BR.vtt 94 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.ar.vtt 92 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.pt-BR.vtt 92 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.pt-BR.vtt 91 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.ja.vtt 91 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.en.vtt 90 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.ar.vtt 90 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.zh-CN.vtt 90 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.en.vtt 90 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.it.vtt 90 B
Part 06-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.zh-CN.vtt 90 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.it.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.pt-PT.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.it.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.it.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.ja.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.pt-BR.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.pt-BR.vtt 89 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.en.vtt 89 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.en.vtt 88 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.it.vtt 88 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.ja.vtt 88 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.ja.vtt 88 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.hr.vtt 87 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.it.vtt 87 B
Part 06-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.zh-CN.vtt 87 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.en.vtt 86 B
Part 06-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.hr.vtt 86 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.en.vtt 86 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.hr.vtt 85 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.zh-Hans.vtt 85 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.hr.vtt 85 B
Part 06-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.zh-CN.vtt 84 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.zh-CN.vtt 84 B
Part 06-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.zh-CN.vtt 83 B
Part 06-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.hr.vtt 83 B
Part 06-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.tr.vtt 81 B
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