[FreeTutorials.Us] Udemy - Hands On Natural Language Processing (NLP) using Python

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  • 6. NLP Core/25. LSA in Python Part 1.mp4 295.56 MB
    5. Numpy and Pandas/1. Introduction to Numpy.mp4 280.68 MB
    6. NLP Core/21. Understanding the N-Gram Model.mp4 259.18 MB
    5. Numpy and Pandas/2. Introduction to Pandas.mp4 251.62 MB
    6. NLP Core/16. Text Modelling using TF-IDF Model.mp4 223.04 MB
    7. Project 1 - Text Classification/9. Understanding Logistic Regression.mp4 201.58 MB
    6. NLP Core/24. Understanding Latent Semantic Analysis.mp4 194.47 MB
    6. NLP Core/26. LSA in Python Part 2.mp4 190.24 MB
    6. NLP Core/22. Building Character N-Gram Model.mp4 185.73 MB
    4. Regular Expressions/5. Shorthand Character Classes.mp4 182.43 MB
    3. Python Crash Course/11. List Comprehension.mp4 165.47 MB
    10. Word2Vec Analysis/1. Understanding Word Vectors.mp4 160.61 MB
    6. NLP Core/23. Building Word N-Gram Model.mp4 160.51 MB
    6. NLP Core/11. Text Modelling using Bag of Words Model.mp4 146.1 MB
    6. NLP Core/7. Stop word removal using NLTK.mp4 139.8 MB
    6. NLP Core/5. Stemming using NLTK.mp4 133.54 MB
    8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.mp4 133.06 MB
    3. Python Crash Course/5. Python Data Structures - Lists.mp4 129.2 MB
    3. Python Crash Course/7. Python Data Structures - Dictionaries.mp4 125.07 MB
    6. NLP Core/18. Building the TF-IDF Model Part 2.mp4 122.73 MB
    6. NLP Core/27. Word Synonyms and Antonyms using NLTK.mp4 117.98 MB
    7. Project 1 - Text Classification/6. Transforming data into BOW Model.mp4 114.68 MB
    6. NLP Core/17. Building the TF-IDF Model Part 1.mp4 109.88 MB
    6. NLP Core/19. Building the TF-IDF Model Part 3.mp4 109.84 MB
    6. NLP Core/8. Parts Of Speech Tagging.mp4 109.11 MB
    10. Word2Vec Analysis/6. Improving the Model.mp4 108.23 MB
    6. NLP Core/15. Building the BOW Model Part 4.mp4 108.07 MB
    6. NLP Core/4. Introduction to Stemming and Lemmatization.mp4 107.55 MB
    8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.mp4 102.74 MB
    9. Project 3 - Text Summarization/7. Calculating the sentence scores.mp4 99.83 MB
    3. Python Crash Course/8. Console and File IO in Python.mp4 97 MB
    7. Project 1 - Text Classification/12. Saving our Model.mp4 96.63 MB
    9. Project 3 - Text Summarization/1. Understanding Text Summarization.mp4 95.68 MB
    9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.mp4 94.27 MB
    3. Python Crash Course/10. Introduction to Classes and Objects.mp4 92.37 MB
    6. NLP Core/28. Word Negation Tracking in Python Part 1.mp4 90.71 MB
    6. NLP Core/12. Building the BOW Model Part 1.mp4 88.59 MB
    7. Project 1 - Text Classification/11. Testing Model performance.mp4 84.05 MB
    6. NLP Core/13. Building the BOW Model Part 2.mp4 82.17 MB
    4. Regular Expressions/3. Finding Patterns in Text Part 2.mp4 81.46 MB
    8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.mp4 80.92 MB
    4. Regular Expressions/2. Finding Patterns in Text Part 1.mp4 79.5 MB
    6. NLP Core/14. Building the BOW Model Part 3.mp4 77 MB
    9. Project 3 - Text Summarization/8. Getting the summary.mp4 76.94 MB
    3. Python Crash Course/9. Introduction to Functions.mp4 76.76 MB
    6. NLP Core/6. Lemmatization using NLTK.mp4 76.47 MB
    1. Introduction to the Course/1. What is NLP.mp4 75.75 MB
    6. NLP Core/2. Tokenizing Words and Sentences.mp4 74.63 MB
    7. Project 1 - Text Classification/8. Creating training and test set.mp4 71.77 MB
    4. Regular Expressions/7. Preprocessing using Regex.mp4 71.64 MB
    7. Project 1 - Text Classification/4. Persisting the dataset.mp4 71.63 MB
    7. Project 1 - Text Classification/5. Preprocessing the data.mp4 67.38 MB
    3. Python Crash Course/3. Introduction to Loops.mp4 64.77 MB
    6. NLP Core/20. Building the TF-IDF Model Part 4.mp4 64.61 MB
    3. Python Crash Course/2. Conditional Statements.mp4 63.77 MB
    4. Regular Expressions/1. Introduction to Regular Expressions.mp4 62.85 MB
    7. Project 1 - Text Classification/1. Getting the data for Text Classification.mp4 62.12 MB
    3. Python Crash Course/4. Loop Control Statements.mp4 62.02 MB
    3. Python Crash Course/6. Python Data Structures - Tuples.mp4 60.92 MB
    3. Python Crash Course/1. Variables and Operations in Python.mp4 60.28 MB
    6. NLP Core/29. Word Negation Tracking in Python Part 2.mp4 58.63 MB
    9. Project 3 - Text Summarization/6. Building the histogram.mp4 58.55 MB
    7. Project 1 - Text Classification/3. Importing the dataset.mp4 57.53 MB
    7. Project 1 - Text Classification/13. Importing and using our Model.mp4 56.12 MB
    6. NLP Core/10. Named Entity Recognition.mp4 56.08 MB
    10. Word2Vec Analysis/2. Importing the data.mp4 54.92 MB
    10. Word2Vec Analysis/5. Testing Model Performance.mp4 54.49 MB
    4. Regular Expressions/4. Substituting Patterns in Text.mp4 54.25 MB
    9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.mp4 50.67 MB
    10. Word2Vec Analysis/7. Exploring Pre-trained Models.mp4 50.42 MB
    9. Project 3 - Text Summarization/4. Preprocessing the data.mp4 48.27 MB
    7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.mp4 47.38 MB
    2. Getting the required softwares/3. A tour of Spyder IDE.mp4 46.82 MB
    8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.mp4 46.71 MB
    9. Project 3 - Text Summarization/2. Fetching article data from the web.mp4 43.91 MB
    10. Word2Vec Analysis/3. Preparing the data.mp4 38.5 MB
    8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.mp4 38.12 MB
    8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.mp4 36.03 MB
    8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.mp4 35.09 MB
    10. Word2Vec Analysis/4. Training the Word2Vec Model.mp4 33.81 MB
    2. Getting the required softwares/1. Installing Anaconda Python.mp4 33.41 MB
    7. Project 1 - Text Classification/10. Training our classifier.mp4 30.69 MB
    6. NLP Core/1. Installing NLTK in Python.mp4 29.31 MB
    8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.mp4 28.34 MB
    1. Introduction to the Course/2. Getting the Course Resources.mp4 18.23 MB
    5. Numpy and Pandas/2. Introduction to Pandas.srt 28.61 KB
    5. Numpy and Pandas/1. Introduction to Numpy.srt 27.1 KB
    6. NLP Core/21. Understanding the N-Gram Model.srt 27.05 KB
    6. NLP Core/25. LSA in Python Part 1.srt 25.9 KB
    5. Numpy and Pandas/2. Introduction to Pandas.vtt 24.73 KB
    6. NLP Core/21. Understanding the N-Gram Model.vtt 23.5 KB
    5. Numpy and Pandas/1. Introduction to Numpy.vtt 23.46 KB
    6. NLP Core/25. LSA in Python Part 1.vtt 22.33 KB
    6. NLP Core/16. Text Modelling using TF-IDF Model.srt 22.05 KB
    7. Project 1 - Text Classification/9. Understanding Logistic Regression.srt 20.38 KB
    6. NLP Core/22. Building Character N-Gram Model.srt 20.24 KB
    6. NLP Core/24. Understanding Latent Semantic Analysis.srt 19.28 KB
    6. NLP Core/16. Text Modelling using TF-IDF Model.vtt 19.17 KB
    7. Project 1 - Text Classification/9. Understanding Logistic Regression.vtt 17.87 KB
    6. NLP Core/22. Building Character N-Gram Model.vtt 17.56 KB
    4. Regular Expressions/5. Shorthand Character Classes.srt 17.29 KB
    6. NLP Core/24. Understanding Latent Semantic Analysis.vtt 16.77 KB
    3. Python Crash Course/11. List Comprehension.srt 16.6 KB
    3. Python Crash Course/5. Python Data Structures - Lists.srt 16.03 KB
    10. Word2Vec Analysis/1. Understanding Word Vectors.srt 16 KB
    4. Regular Expressions/5. Shorthand Character Classes.vtt 14.99 KB
    6. NLP Core/26. LSA in Python Part 2.srt 14.85 KB
    6. NLP Core/23. Building Word N-Gram Model.srt 14.76 KB
    6. NLP Core/11. Text Modelling using Bag of Words Model.srt 14.71 KB
    3. Python Crash Course/11. List Comprehension.vtt 14.31 KB
    3. Python Crash Course/7. Python Data Structures - Dictionaries.srt 14.22 KB
    10. Word2Vec Analysis/1. Understanding Word Vectors.vtt 13.98 KB
    3. Python Crash Course/5. Python Data Structures - Lists.vtt 13.92 KB
    6. NLP Core/27. Word Synonyms and Antonyms using NLTK.srt 13.15 KB
    6. NLP Core/26. LSA in Python Part 2.vtt 12.89 KB
    6. NLP Core/23. Building Word N-Gram Model.vtt 12.88 KB
    6. NLP Core/11. Text Modelling using Bag of Words Model.vtt 12.76 KB
    6. NLP Core/28. Word Negation Tracking in Python Part 1.srt 12.67 KB
    3. Python Crash Course/7. Python Data Structures - Dictionaries.vtt 12.4 KB
    6. NLP Core/27. Word Synonyms and Antonyms using NLTK.vtt 11.44 KB
    6. NLP Core/28. Word Negation Tracking in Python Part 1.vtt 11.04 KB
    4. Regular Expressions/2. Finding Patterns in Text Part 1.srt 10.92 KB
    6. NLP Core/4. Introduction to Stemming and Lemmatization.srt 10.05 KB
    4. Regular Expressions/3. Finding Patterns in Text Part 2.srt 9.94 KB
    3. Python Crash Course/3. Introduction to Loops.srt 9.84 KB
    9. Project 3 - Text Summarization/1. Understanding Text Summarization.srt 9.76 KB
    7. Project 1 - Text Classification/6. Transforming data into BOW Model.srt 9.74 KB
    3. Python Crash Course/8. Console and File IO in Python.srt 9.73 KB
    3. Python Crash Course/1. Variables and Operations in Python.srt 9.49 KB
    4. Regular Expressions/2. Finding Patterns in Text Part 1.vtt 9.48 KB
    9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.srt 9.46 KB
    6. NLP Core/18. Building the TF-IDF Model Part 2.srt 9.42 KB
    3. Python Crash Course/10. Introduction to Classes and Objects.srt 9.38 KB
    3. Python Crash Course/4. Loop Control Statements.srt 9.36 KB
    6. NLP Core/4. Introduction to Stemming and Lemmatization.vtt 8.79 KB
    8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.srt 8.69 KB
    4. Regular Expressions/3. Finding Patterns in Text Part 2.vtt 8.62 KB
    6. NLP Core/7. Stop word removal using NLTK.srt 8.58 KB
    3. Python Crash Course/3. Introduction to Loops.vtt 8.57 KB
    7. Project 1 - Text Classification/6. Transforming data into BOW Model.vtt 8.57 KB
    9. Project 3 - Text Summarization/1. Understanding Text Summarization.vtt 8.51 KB
    6. NLP Core/5. Stemming using NLTK.srt 8.47 KB
    6. NLP Core/15. Building the BOW Model Part 4.srt 8.44 KB
    3. Python Crash Course/8. Console and File IO in Python.vtt 8.38 KB
    6. NLP Core/19. Building the TF-IDF Model Part 3.srt 8.37 KB
    3. Python Crash Course/9. Introduction to Functions.srt 8.29 KB
    3. Python Crash Course/1. Variables and Operations in Python.vtt 8.27 KB
    6. NLP Core/18. Building the TF-IDF Model Part 2.vtt 8.23 KB
    3. Python Crash Course/10. Introduction to Classes and Objects.vtt 8.21 KB
    6. NLP Core/17. Building the TF-IDF Model Part 1.srt 8.21 KB
    9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.vtt 8.18 KB
    3. Python Crash Course/4. Loop Control Statements.vtt 8.15 KB
    6. NLP Core/29. Word Negation Tracking in Python Part 2.srt 8.12 KB
    4. Regular Expressions/4. Substituting Patterns in Text.srt 8.07 KB
    4. Regular Expressions/7. Preprocessing using Regex.srt 7.98 KB
    9. Project 3 - Text Summarization/7. Calculating the sentence scores.srt 7.92 KB
    10. Word2Vec Analysis/6. Improving the Model.srt 7.83 KB
    6. NLP Core/8. Parts Of Speech Tagging.srt 7.83 KB
    7. Project 1 - Text Classification/12. Saving our Model.srt 7.75 KB
    1. Introduction to the Course/1. What is NLP.srt 7.65 KB
    7. Project 1 - Text Classification/1. Getting the data for Text Classification.srt 7.61 KB
    8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.vtt 7.57 KB
    6. NLP Core/7. Stop word removal using NLTK.vtt 7.52 KB
    6. NLP Core/5. Stemming using NLTK.vtt 7.4 KB
    6. NLP Core/15. Building the BOW Model Part 4.vtt 7.36 KB
    6. NLP Core/19. Building the TF-IDF Model Part 3.vtt 7.32 KB
    3. Python Crash Course/9. Introduction to Functions.vtt 7.21 KB
    6. NLP Core/17. Building the TF-IDF Model Part 1.vtt 7.19 KB
    7. Project 1 - Text Classification/11. Testing Model performance.srt 7.19 KB
    3. Python Crash Course/6. Python Data Structures - Tuples.srt 7.07 KB
    6. NLP Core/29. Word Negation Tracking in Python Part 2.vtt 7.07 KB
    8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.srt 6.98 KB
    4. Regular Expressions/4. Substituting Patterns in Text.vtt 6.96 KB
    3. Python Crash Course/2. Conditional Statements.srt 6.96 KB
    9. Project 3 - Text Summarization/7. Calculating the sentence scores.vtt 6.96 KB
    4. Regular Expressions/7. Preprocessing using Regex.vtt 6.92 KB
    6. NLP Core/10. Named Entity Recognition.srt 6.84 KB
    7. Project 1 - Text Classification/12. Saving our Model.vtt 6.78 KB
    6. NLP Core/8. Parts Of Speech Tagging.vtt 6.77 KB
    10. Word2Vec Analysis/7. Exploring Pre-trained Models.srt 6.74 KB
    8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.srt 6.73 KB
    10. Word2Vec Analysis/6. Improving the Model.vtt 6.7 KB
    7. Project 1 - Text Classification/1. Getting the data for Text Classification.vtt 6.68 KB
    1. Introduction to the Course/1. What is NLP.vtt 6.67 KB
    7. Project 1 - Text Classification/3. Importing the dataset.srt 6.62 KB
    10. Word2Vec Analysis/2. Importing the data.srt 6.47 KB
    7. Project 1 - Text Classification/4. Persisting the dataset.srt 6.46 KB
    7. Project 1 - Text Classification/11. Testing Model performance.vtt 6.25 KB
    4. Regular Expressions/1. Introduction to Regular Expressions.srt 6.13 KB
    3. Python Crash Course/6. Python Data Structures - Tuples.vtt 6.11 KB
    2. Getting the required softwares/3. A tour of Spyder IDE.srt 6.09 KB
    3. Python Crash Course/2. Conditional Statements.vtt 6.06 KB
    8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.vtt 6.04 KB
    7. Project 1 - Text Classification/5. Preprocessing the data.srt 6.02 KB
    6. NLP Core/13. Building the BOW Model Part 2.srt 6.01 KB
    6. NLP Core/10. Named Entity Recognition.vtt 6.01 KB
    9. Project 3 - Text Summarization/8. Getting the summary.srt 5.95 KB
    10. Word2Vec Analysis/7. Exploring Pre-trained Models.vtt 5.92 KB
    9. Project 3 - Text Summarization/2. Fetching article data from the web.srt 5.89 KB
    8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.vtt 5.88 KB
    7. Project 1 - Text Classification/3. Importing the dataset.vtt 5.76 KB
    6. NLP Core/14. Building the BOW Model Part 3.srt 5.72 KB
    7. Project 1 - Text Classification/8. Creating training and test set.srt 5.7 KB
    7. Project 1 - Text Classification/4. Persisting the dataset.vtt 5.67 KB
    10. Word2Vec Analysis/2. Importing the data.vtt 5.6 KB
    6. NLP Core/12. Building the BOW Model Part 1.srt 5.45 KB
    9. Project 3 - Text Summarization/6. Building the histogram.srt 5.45 KB
    4. Regular Expressions/1. Introduction to Regular Expressions.vtt 5.39 KB
    6. NLP Core/2. Tokenizing Words and Sentences.srt 5.34 KB
    6. NLP Core/1. Installing NLTK in Python.srt 5.32 KB
    2. Getting the required softwares/3. A tour of Spyder IDE.vtt 5.31 KB
    8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.srt 5.27 KB
    6. NLP Core/20. Building the TF-IDF Model Part 4.srt 5.27 KB
    6. NLP Core/13. Building the BOW Model Part 2.vtt 5.25 KB
    7. Project 1 - Text Classification/5. Preprocessing the data.vtt 5.23 KB
    9. Project 3 - Text Summarization/8. Getting the summary.vtt 5.16 KB
    9. Project 3 - Text Summarization/2. Fetching article data from the web.vtt 5.14 KB
    7. Project 1 - Text Classification/8. Creating training and test set.vtt 4.98 KB
    7. Project 1 - Text Classification/13. Importing and using our Model.srt 4.98 KB
    6. NLP Core/14. Building the BOW Model Part 3.vtt 4.96 KB
    8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.srt 4.95 KB
    10. Word2Vec Analysis/5. Testing Model Performance.srt 4.94 KB
    6. NLP Core/12. Building the BOW Model Part 1.vtt 4.77 KB
    9. Project 3 - Text Summarization/6. Building the histogram.vtt 4.75 KB
    6. NLP Core/1. Installing NLTK in Python.vtt 4.72 KB
    6. NLP Core/2. Tokenizing Words and Sentences.vtt 4.67 KB
    8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.vtt 4.6 KB
    6. NLP Core/20. Building the TF-IDF Model Part 4.vtt 4.58 KB
    8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.srt 4.56 KB
    6. NLP Core/6. Lemmatization using NLTK.srt 4.48 KB
    9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.srt 4.47 KB
    2. Getting the required softwares/1. Installing Anaconda Python.srt 4.45 KB
    8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.vtt 4.33 KB
    7. Project 1 - Text Classification/13. Importing and using our Model.vtt 4.33 KB
    10. Word2Vec Analysis/5. Testing Model Performance.vtt 4.26 KB
    10. Word2Vec Analysis/3. Preparing the data.srt 4.14 KB
    9. Project 3 - Text Summarization/4. Preprocessing the data.srt 4.05 KB
    8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.vtt 3.98 KB
    2. Getting the required softwares/1. Installing Anaconda Python.vtt 3.93 KB
    9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.vtt 3.92 KB
    7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.srt 3.86 KB
    6. NLP Core/6. Lemmatization using NLTK.vtt 3.85 KB
    10. Word2Vec Analysis/3. Preparing the data.vtt 3.57 KB
    9. Project 3 - Text Summarization/4. Preprocessing the data.vtt 3.56 KB
    10. Word2Vec Analysis/4. Training the Word2Vec Model.srt 3.49 KB
    7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.vtt 3.35 KB
    6. NLP Core/9. POS Tag Meanings.html 3.32 KB
    10. Word2Vec Analysis/4. Training the Word2Vec Model.vtt 3.02 KB
    8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.srt 2.53 KB
    7. Project 1 - Text Classification/10. Training our classifier.srt 2.3 KB
    8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.srt 2.28 KB
    8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.vtt 2.2 KB
    1. Introduction to the Course/2. Getting the Course Resources.srt 2.06 KB
    7. Project 1 - Text Classification/10. Training our classifier.vtt 2.01 KB
    8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.vtt 2 KB
    1. Introduction to the Course/2. Getting the Course Resources.vtt 1.83 KB
    2. Getting the required softwares/4. How to take this course.html 1.62 KB
    6. NLP Core/3. How tokenization works - Text.html 1.6 KB
    4. Regular Expressions/6. Character Ranges - Text.html 1.2 KB
    7. Project 1 - Text Classification/2. Getting the data for Text Classification - Text.html 806 B
    2. Getting the required softwares/2. Installing Anaconda Python - Text.html 734 B
    11. Conclusion/1. Where you go from here.html 727 B
    1. Introduction to the Course/3. Getting the Course Resources - Text.html 614 B
    [FTU Forum].url 252 B
    3. Python Crash Course/12. Test Your Skills.html 156 B
    4. Regular Expressions/8. Test Your Skills.html 156 B
    [FreeCoursesOnline.Me].url 133 B
    [FreeTutorials.Us].url 119 B

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