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6 - Click-Through Prediction with Logistic Regression#/Click-Through Prediction with Logistic Regression by Gradient Descent.mp4 75.32 MB
3 - Spam Email Detection with Naïve Bayes#/The Naïve Bayes Implementation.mp4 57.36 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Touring Powerful NLP Libraries in Python.mp4 40.29 MB
6 - Click-Through Prediction with Logistic Regression#/Logistic Regression Classifier.mp4 37.4 MB
3 - Spam Email Detection with Naïve Bayes#/Classifier Performance Evaluation.mp4 36.98 MB
5 - Click-Through Prediction with Tree-Based Algorithms/Decision Tree Classifier.mp4 36.69 MB
4 - News Topic Classification with Support Vector Machine/News topic Classification with Support Vector Machine.mp4 36.37 MB
7 - Stock Price Prediction with Regression Algorithms/Stock Price Prediction with Regression Algorithms.mp4 34.23 MB
8 - Best Practices/Best Practices in Data Preparation Stage.mp4 31.86 MB
7 - Stock Price Prediction with Regression Algorithms/Linear Regression.mp4 30.28 MB
7 - Stock Price Prediction with Regression Algorithms/Decision Tree Regression.mp4 27.45 MB
5 - Click-Through Prediction with Tree-Based Algorithms/Click-Through Prediction with Decision Tree.mp4 24.99 MB
7 - Stock Price Prediction with Regression Algorithms/Predicting Stock Price with Regression Algorithms.mp4 24.42 MB
5 - Click-Through Prediction with Tree-Based Algorithms/The Implementations of Decision Tree.mp4 22.81 MB
1 - Getting Started with Python and Machine Learning/Installing Software and Setting Up.mp4 22.01 MB
4 - News Topic Classification with Support Vector Machine/Fetal State Classification with SVM.mp4 21.84 MB
6 - Click-Through Prediction with Logistic Regression#/One-Hot Encoding - Converting Categorical Features to Numerical.mp4 21.42 MB
8 - Best Practices/Best Practices in the Training Sets Generation Stage.mp4 20.46 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Thinking about Features.mp4 20.34 MB
4 - News Topic Classification with Support Vector Machine/The Implementations of SVM.mp4 19.68 MB
5 - Click-Through Prediction with Tree-Based Algorithms/Random Forest - Feature Bagging of Decision Tree.mp4 18.28 MB
3 - Spam Email Detection with Naïve Bayes#/Model Tuning and cross-validation.mp4 18.26 MB
1 - Getting Started with Python and Machine Learning/The Course Overview.mp4 17.18 MB
4 - News Topic Classification with Support Vector Machine/Recap and Inverse Document Frequency.mp4 16.63 MB
6 - Click-Through Prediction with Logistic Regression#/Feature Selection via Random Forest.mp4 16.04 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Understanding NLP.mp4 15.97 MB
4 - News Topic Classification with Support Vector Machine/Choosing Between the Linear and the RBF Kernel.mp4 14.21 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Getting the Newsgroups Data.mp4 14.1 MB
8 - Best Practices/Best Practices in the Deployment and Monitoring Stage.mp4 13.9 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Topic Modeling.mp4 13.04 MB
1 - Getting Started with Python and Machine Learning/Introduction to Machine Learning.mp4 13.02 MB
7 - Stock Price Prediction with Regression Algorithms/Regression Performance Evaluation.mp4 12.72 MB
7 - Stock Price Prediction with Regression Algorithms/Data Acquisition and Feature Generation.mp4 12.29 MB
4 - News Topic Classification with Support Vector Machine/The Kernels of SVM.mp4 11.79 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Visualization.mp4 11.53 MB
5 - Click-Through Prediction with Tree-Based Algorithms/Brief Overview of Advertising Click-Through Prediction.mp4 11 MB
8 - Best Practices/Best Practices in the Model Training, Evaluation, and Selection Stage.mp4 10.84 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Clustering.mp4 10.44 MB
4 - News Topic Classification with Support Vector Machine/The Mechanics of SVM.mp4 9.16 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Data Preprocessing.mp4 9.15 MB
3 - Spam Email Detection with Naïve Bayes#/Getting Started with Classification.mp4 8.76 MB
7 - Stock Price Prediction with Regression Algorithms/Support Vector Regression.mp4 8.09 MB
3 - Spam Email Detection with Naïve Bayes#/The Mechanics of Naïve Bayes.mp4 7.33 MB
7 - Stock Price Prediction with Regression Algorithms/Brief Overview of the Stock Market And Stock Price.mp4 7.05 MB
3 - Spam Email Detection with Naïve Bayes#/Exploring Naïve Bayes.mp4 5.1 MB
V09050_Code/V09050_Code/Section 04/CTG.xls 1.66 MB
2 - Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms/Machine Learning with Python.mp4 1.61 MB
7 - Stock Price Prediction with Regression Algorithms/Machine Learning with Python.mp4 1.61 MB
V09050_Code/V09050_Code/Section 07/198810101_20151231.csv 543.79 KB
V09050_Code/V09050_Code/Section 03/email_spam.py 10.28 KB
V09050_Code/V09050_Code/Section 05/1decision_tree_submit.py 9.81 KB
V09050_Code/V09050_Code/Section 06/3logistic_regression_from_scratch.py 7.6 KB
V09050_Code/V09050_Code/Section 07/1stock_price_prediction.py 7.48 KB
V09050_Code/V09050_Code/Section 07/3decision_tree_regression.py 7.08 KB
V09050_Code/V09050_Code/Section 03/.DS_Store 6 KB
V09050_Code/V09050_Code/Section 04/.DS_Store 6 KB
V09050_Code/V09050_Code/Section 05/.DS_Store 6 KB
V09050_Code/V09050_Code/Section 06/.DS_Store 6 KB
V09050_Code/V09050_Code/Section 07/.DS_Store 6 KB
V09050_Code/V09050_Code/Section 08/.DS_Store 6 KB
V09050_Code/V09050_Code/Section 06/5scikit_logistic_regression.py 5.24 KB
V09050_Code/V09050_Code/Section 04/2topic_categorization.py 5.04 KB
V09050_Code/V09050_Code/Section 07/2linear_regression.py 4.72 KB
V09050_Code/V09050_Code/Section 02/.ropeproject/config.py 3.38 KB
V09050_Code/V09050_Code/Section 08/1imputation.py 3.25 KB
V09050_Code/V09050_Code/Section 04/1email_spam_tfidf_submit.py 2.62 KB
V09050_Code/V09050_Code/Section 06/1one_hot_encode.py 2.42 KB
V09050_Code/V09050_Code/Section 02/.ropeproject/objectdb 2.21 KB
V09050_Code/V09050_Code/Section 05/2avazu_ctr.py 2.06 KB
V09050_Code/V09050_Code/Section 06/4random_forest_feature_selection.py 1.68 KB
V09050_Code/V09050_Code/Section 04/4ctg.py 1.14 KB
V09050_Code/V09050_Code/Section 04/3plot_rbf_kernels.py 1.13 KB
V09050_Code/V09050_Code/Section 08/2feature_selection.py 1.12 KB
V09050_Code/V09050_Code/Section 08/5save_reuse_monitor_model.py 1 KB
V09050_Code/V09050_Code/Section 02/.ropeproject/globalnames 1011 B
V09050_Code/V09050_Code/Section 02/4topic_model.py 998 B
V09050_Code/V09050_Code/Section 02/3post_clustering.py 919 B
V09050_Code/V09050_Code/Section 06/2logistic_function.py 833 B
V09050_Code/V09050_Code/Section 02/2clean_words.py 723 B
V09050_Code/V09050_Code/Section 07/20051201_20151210.csv 644 B
V09050_Code/V09050_Code/Section 08/3dimensionality_reduction.py 635 B
V09050_Code/V09050_Code/Section 02/1histogram.py 529 B
V09050_Code/V09050_Code/Section 07/4support_vector_regression.py 439 B
V09050_Code/V09050_Code/Section 08/4generic_feature_engineering.py 344 B
V09050_Code/V09050_Code/Section 02/0_getting.py 303 B
V09050_Code/V09050_Code/Section 02/.ropeproject/history 14 B
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