[FreeCourseSite.com] Udemy - Hands On Natural Language Processing (NLP) using Python

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[FreeCourseSite.com] Udemy - Hands On Natural Language Processing (NLP) using Python

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种子哈希：9ffd6efe9d0aaf14f7b98e386013baf1a279be93
文件大小： 7.99G
已经下载：770次
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收录时间：2021-04-08
最近下载：2024-10-09

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文件列表

  • 6. NLP Core/25. LSA in Python Part 1.mp4 309.9 MB
  • 5. Numpy and Pandas/1. Introduction to Numpy.mp4 294.3 MB
  • 6. NLP Core/21. Understanding the N-Gram Model.mp4 271.8 MB
  • 5. Numpy and Pandas/2. Introduction to Pandas.mp4 263.8 MB
  • 6. NLP Core/16. Text Modelling using TF-IDF Model.mp4 233.9 MB
  • 7. Project 1 - Text Classification/9. Understanding Logistic Regression.mp4 211.4 MB
  • 6. NLP Core/24. Understanding Latent Semantic Analysis.mp4 203.9 MB
  • 6. NLP Core/26. LSA in Python Part 2.mp4 199.5 MB
  • 6. NLP Core/22. Building Character N-Gram Model.mp4 194.7 MB
  • 4. Regular Expressions/5. Shorthand Character Classes.mp4 191.3 MB
  • 3. Python Crash Course/11. List Comprehension.mp4 173.5 MB
  • 10. Word2Vec Analysis/1. Understanding Word Vectors.mp4 168.4 MB
  • 6. NLP Core/23. Building Word N-Gram Model.mp4 168.3 MB
  • 6. NLP Core/11. Text Modelling using Bag of Words Model.mp4 153.2 MB
  • 6. NLP Core/7. Stop word removal using NLTK.mp4 146.6 MB
  • 6. NLP Core/5. Stemming using NLTK.mp4 140.0 MB
  • 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.mp4 139.5 MB
  • 3. Python Crash Course/5. Python Data Structures - Lists.mp4 135.5 MB
  • 3. Python Crash Course/7. Python Data Structures - Dictionaries.mp4 131.1 MB
  • 6. NLP Core/18. Building the TF-IDF Model Part 2.mp4 128.7 MB
  • 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.mp4 123.7 MB
  • 7. Project 1 - Text Classification/6. Transforming data into BOW Model.mp4 120.3 MB
  • 6. NLP Core/17. Building the TF-IDF Model Part 1.mp4 115.2 MB
  • 6. NLP Core/19. Building the TF-IDF Model Part 3.mp4 115.2 MB
  • 6. NLP Core/8. Parts Of Speech Tagging.mp4 114.4 MB
  • 10. Word2Vec Analysis/6. Improving the Model.mp4 113.5 MB
  • 6. NLP Core/15. Building the BOW Model Part 4.mp4 113.3 MB
  • 6. NLP Core/4. Introduction to Stemming and Lemmatization.mp4 112.8 MB
  • 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.mp4 107.7 MB
  • 9. Project 3 - Text Summarization/7. Calculating the sentence scores.mp4 104.7 MB
  • 3. Python Crash Course/8. Console and File IO in Python.mp4 101.7 MB
  • 7. Project 1 - Text Classification/12. Saving our Model.mp4 101.3 MB
  • 9. Project 3 - Text Summarization/1. Understanding Text Summarization.mp4 100.3 MB
  • 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.mp4 98.8 MB
  • 3. Python Crash Course/10. Introduction to Classes and Objects.mp4 96.9 MB
  • 6. NLP Core/28. Word Negation Tracking in Python Part 1.mp4 95.1 MB
  • 6. NLP Core/12. Building the BOW Model Part 1.mp4 92.9 MB
  • 7. Project 1 - Text Classification/11. Testing Model performance.mp4 88.1 MB
  • 6. NLP Core/13. Building the BOW Model Part 2.mp4 86.2 MB
  • 4. Regular Expressions/3. Finding Patterns in Text Part 2.mp4 85.4 MB
  • 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.mp4 84.9 MB
  • 4. Regular Expressions/2. Finding Patterns in Text Part 1.mp4 83.4 MB
  • 6. NLP Core/14. Building the BOW Model Part 3.mp4 80.7 MB
  • 9. Project 3 - Text Summarization/8. Getting the summary.mp4 80.7 MB
  • 3. Python Crash Course/9. Introduction to Functions.mp4 80.5 MB
  • 6. NLP Core/6. Lemmatization using NLTK.mp4 80.2 MB
  • 1. Introduction to the Course/1. What is NLP.mp4 79.4 MB
  • 6. NLP Core/2. Tokenizing Words and Sentences.mp4 78.3 MB
  • 7. Project 1 - Text Classification/8. Creating training and test set.mp4 75.3 MB
  • 4. Regular Expressions/7. Preprocessing using Regex.mp4 75.1 MB
  • 7. Project 1 - Text Classification/4. Persisting the dataset.mp4 75.1 MB
  • 7. Project 1 - Text Classification/5. Preprocessing the data.mp4 70.7 MB
  • 3. Python Crash Course/3. Introduction to Loops.mp4 67.9 MB
  • 6. NLP Core/20. Building the TF-IDF Model Part 4.mp4 67.7 MB
  • 3. Python Crash Course/2. Conditional Statements.mp4 66.9 MB
  • 4. Regular Expressions/1. Introduction to Regular Expressions.mp4 65.9 MB
  • 7. Project 1 - Text Classification/1. Getting the data for Text Classification.mp4 65.1 MB
  • 3. Python Crash Course/4. Loop Control Statements.mp4 65.0 MB
  • 3. Python Crash Course/6. Python Data Structures - Tuples.mp4 63.9 MB
  • 3. Python Crash Course/1. Variables and Operations in Python.mp4 63.2 MB
  • 6. NLP Core/29. Word Negation Tracking in Python Part 2.mp4 61.5 MB
  • 9. Project 3 - Text Summarization/6. Building the histogram.mp4 61.4 MB
  • 7. Project 1 - Text Classification/3. Importing the dataset.mp4 60.3 MB
  • 7. Project 1 - Text Classification/13. Importing and using our Model.mp4 58.8 MB
  • 6. NLP Core/10. Named Entity Recognition.mp4 58.8 MB
  • 10. Word2Vec Analysis/2. Importing the data.mp4 57.6 MB
  • 10. Word2Vec Analysis/5. Testing Model Performance.mp4 57.1 MB
  • 4. Regular Expressions/4. Substituting Patterns in Text.mp4 56.9 MB
  • 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.mp4 53.1 MB
  • 10. Word2Vec Analysis/7. Exploring Pre-trained Models.mp4 52.9 MB
  • 9. Project 3 - Text Summarization/4. Preprocessing the data.mp4 50.6 MB
  • 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.mp4 49.7 MB
  • 2. Getting the required softwares/3. A tour of Spyder IDE.mp4 49.1 MB
  • 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.mp4 49.0 MB
  • 9. Project 3 - Text Summarization/2. Fetching article data from the web.mp4 46.0 MB
  • 10. Word2Vec Analysis/3. Preparing the data.mp4 40.4 MB
  • 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.mp4 40.0 MB
  • 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.mp4 37.8 MB
  • 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.mp4 36.8 MB
  • 10. Word2Vec Analysis/4. Training the Word2Vec Model.mp4 35.5 MB
  • 2. Getting the required softwares/1. Installing Anaconda Python.mp4 35.0 MB
  • 7. Project 1 - Text Classification/10. Training our classifier.mp4 32.2 MB
  • 6. NLP Core/1. Installing NLTK in Python.mp4 30.7 MB
  • 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.mp4 29.7 MB
  • 1. Introduction to the Course/2. Getting the Course Resources.mp4 19.1 MB
  • 5. Numpy and Pandas/2. Introduction to Pandas.srt 29.3 kB
  • 5. Numpy and Pandas/1. Introduction to Numpy.srt 27.7 kB
  • 6. NLP Core/21. Understanding the N-Gram Model.srt 27.7 kB
  • 6. NLP Core/25. LSA in Python Part 1.srt 26.5 kB
  • 5. Numpy and Pandas/2. Introduction to Pandas.vtt 25.3 kB
  • 6. NLP Core/21. Understanding the N-Gram Model.vtt 24.1 kB
  • 5. Numpy and Pandas/1. Introduction to Numpy.vtt 24.0 kB
  • 6. NLP Core/25. LSA in Python Part 1.vtt 22.9 kB
  • 6. NLP Core/16. Text Modelling using TF-IDF Model.srt 22.6 kB
  • 7. Project 1 - Text Classification/9. Understanding Logistic Regression.srt 20.9 kB
  • 6. NLP Core/22. Building Character N-Gram Model.srt 20.7 kB
  • 6. NLP Core/24. Understanding Latent Semantic Analysis.srt 19.7 kB
  • 6. NLP Core/16. Text Modelling using TF-IDF Model.vtt 19.6 kB
  • 7. Project 1 - Text Classification/9. Understanding Logistic Regression.vtt 18.3 kB
  • 6. NLP Core/22. Building Character N-Gram Model.vtt 18.0 kB
  • 4. Regular Expressions/5. Shorthand Character Classes.srt 17.7 kB
  • 6. NLP Core/24. Understanding Latent Semantic Analysis.vtt 17.2 kB
  • 3. Python Crash Course/11. List Comprehension.srt 17.0 kB
  • 3. Python Crash Course/5. Python Data Structures - Lists.srt 16.4 kB
  • 10. Word2Vec Analysis/1. Understanding Word Vectors.srt 16.4 kB
  • 4. Regular Expressions/5. Shorthand Character Classes.vtt 15.4 kB
  • 6. NLP Core/26. LSA in Python Part 2.srt 15.2 kB
  • 6. NLP Core/23. Building Word N-Gram Model.srt 15.1 kB
  • 6. NLP Core/11. Text Modelling using Bag of Words Model.srt 15.1 kB
  • 3. Python Crash Course/11. List Comprehension.vtt 14.7 kB
  • 3. Python Crash Course/7. Python Data Structures - Dictionaries.srt 14.6 kB
  • 10. Word2Vec Analysis/1. Understanding Word Vectors.vtt 14.3 kB
  • 3. Python Crash Course/5. Python Data Structures - Lists.vtt 14.3 kB
  • 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.srt 13.5 kB
  • 6. NLP Core/26. LSA in Python Part 2.vtt 13.2 kB
  • 6. NLP Core/23. Building Word N-Gram Model.vtt 13.2 kB
  • 6. NLP Core/11. Text Modelling using Bag of Words Model.vtt 13.1 kB
  • 6. NLP Core/28. Word Negation Tracking in Python Part 1.srt 13.0 kB
  • 3. Python Crash Course/7. Python Data Structures - Dictionaries.vtt 12.7 kB
  • 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.vtt 11.7 kB
  • 6. NLP Core/28. Word Negation Tracking in Python Part 1.vtt 11.3 kB
  • 4. Regular Expressions/2. Finding Patterns in Text Part 1.srt 11.2 kB
  • 6. NLP Core/4. Introduction to Stemming and Lemmatization.srt 10.3 kB
  • 4. Regular Expressions/3. Finding Patterns in Text Part 2.srt 10.2 kB
  • 3. Python Crash Course/3. Introduction to Loops.srt 10.1 kB
  • 9. Project 3 - Text Summarization/1. Understanding Text Summarization.srt 10.0 kB
  • 7. Project 1 - Text Classification/6. Transforming data into BOW Model.srt 10.0 kB
  • 3. Python Crash Course/8. Console and File IO in Python.srt 10.0 kB
  • 3. Python Crash Course/1. Variables and Operations in Python.srt 9.7 kB
  • 4. Regular Expressions/2. Finding Patterns in Text Part 1.vtt 9.7 kB
  • 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.srt 9.7 kB
  • 6. NLP Core/18. Building the TF-IDF Model Part 2.srt 9.6 kB
  • 3. Python Crash Course/10. Introduction to Classes and Objects.srt 9.6 kB
  • 3. Python Crash Course/4. Loop Control Statements.srt 9.6 kB
  • 6. NLP Core/4. Introduction to Stemming and Lemmatization.vtt 9.0 kB
  • 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.srt 8.9 kB
  • 4. Regular Expressions/3. Finding Patterns in Text Part 2.vtt 8.8 kB
  • 6. NLP Core/7. Stop word removal using NLTK.srt 8.8 kB
  • 3. Python Crash Course/3. Introduction to Loops.vtt 8.8 kB
  • 7. Project 1 - Text Classification/6. Transforming data into BOW Model.vtt 8.8 kB
  • 9. Project 3 - Text Summarization/1. Understanding Text Summarization.vtt 8.7 kB
  • 6. NLP Core/5. Stemming using NLTK.srt 8.7 kB
  • 6. NLP Core/15. Building the BOW Model Part 4.srt 8.6 kB
  • 3. Python Crash Course/8. Console and File IO in Python.vtt 8.6 kB
  • 6. NLP Core/19. Building the TF-IDF Model Part 3.srt 8.6 kB
  • 3. Python Crash Course/9. Introduction to Functions.srt 8.5 kB
  • 3. Python Crash Course/1. Variables and Operations in Python.vtt 8.5 kB
  • 6. NLP Core/18. Building the TF-IDF Model Part 2.vtt 8.4 kB
  • 3. Python Crash Course/10. Introduction to Classes and Objects.vtt 8.4 kB
  • 6. NLP Core/17. Building the TF-IDF Model Part 1.srt 8.4 kB
  • 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.vtt 8.4 kB
  • 3. Python Crash Course/4. Loop Control Statements.vtt 8.3 kB
  • 6. NLP Core/29. Word Negation Tracking in Python Part 2.srt 8.3 kB
  • 4. Regular Expressions/4. Substituting Patterns in Text.srt 8.3 kB
  • 4. Regular Expressions/7. Preprocessing using Regex.srt 8.2 kB
  • 9. Project 3 - Text Summarization/7. Calculating the sentence scores.srt 8.1 kB
  • 10. Word2Vec Analysis/6. Improving the Model.srt 8.0 kB
  • 6. NLP Core/8. Parts Of Speech Tagging.srt 8.0 kB
  • 7. Project 1 - Text Classification/12. Saving our Model.srt 7.9 kB
  • 1. Introduction to the Course/1. What is NLP.srt 7.8 kB
  • 7. Project 1 - Text Classification/1. Getting the data for Text Classification.srt 7.8 kB
  • 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.vtt 7.8 kB
  • 6. NLP Core/7. Stop word removal using NLTK.vtt 7.7 kB
  • 6. NLP Core/5. Stemming using NLTK.vtt 7.6 kB
  • 6. NLP Core/15. Building the BOW Model Part 4.vtt 7.5 kB
  • 6. NLP Core/19. Building the TF-IDF Model Part 3.vtt 7.5 kB
  • 3. Python Crash Course/9. Introduction to Functions.vtt 7.4 kB
  • 6. NLP Core/17. Building the TF-IDF Model Part 1.vtt 7.4 kB
  • 7. Project 1 - Text Classification/11. Testing Model performance.srt 7.4 kB
  • 3. Python Crash Course/6. Python Data Structures - Tuples.srt 7.2 kB
  • 6. NLP Core/29. Word Negation Tracking in Python Part 2.vtt 7.2 kB
  • 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.srt 7.1 kB
  • 4. Regular Expressions/4. Substituting Patterns in Text.vtt 7.1 kB
  • 3. Python Crash Course/2. Conditional Statements.srt 7.1 kB
  • 9. Project 3 - Text Summarization/7. Calculating the sentence scores.vtt 7.1 kB
  • 4. Regular Expressions/7. Preprocessing using Regex.vtt 7.1 kB
  • 6. NLP Core/10. Named Entity Recognition.srt 7.0 kB
  • 7. Project 1 - Text Classification/12. Saving our Model.vtt 6.9 kB
  • 6. NLP Core/8. Parts Of Speech Tagging.vtt 6.9 kB
  • 10. Word2Vec Analysis/7. Exploring Pre-trained Models.srt 6.9 kB
  • 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.srt 6.9 kB
  • 10. Word2Vec Analysis/6. Improving the Model.vtt 6.9 kB
  • 7. Project 1 - Text Classification/1. Getting the data for Text Classification.vtt 6.8 kB
  • 1. Introduction to the Course/1. What is NLP.vtt 6.8 kB
  • 7. Project 1 - Text Classification/3. Importing the dataset.srt 6.8 kB
  • 10. Word2Vec Analysis/2. Importing the data.srt 6.6 kB
  • 7. Project 1 - Text Classification/4. Persisting the dataset.srt 6.6 kB
  • 7. Project 1 - Text Classification/11. Testing Model performance.vtt 6.4 kB
  • 4. Regular Expressions/1. Introduction to Regular Expressions.srt 6.3 kB
  • 3. Python Crash Course/6. Python Data Structures - Tuples.vtt 6.3 kB
  • 2. Getting the required softwares/3. A tour of Spyder IDE.srt 6.2 kB
  • 3. Python Crash Course/2. Conditional Statements.vtt 6.2 kB
  • 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.vtt 6.2 kB
  • 7. Project 1 - Text Classification/5. Preprocessing the data.srt 6.2 kB
  • 6. NLP Core/13. Building the BOW Model Part 2.srt 6.2 kB
  • 6. NLP Core/10. Named Entity Recognition.vtt 6.2 kB
  • 9. Project 3 - Text Summarization/8. Getting the summary.srt 6.1 kB
  • 10. Word2Vec Analysis/7. Exploring Pre-trained Models.vtt 6.1 kB
  • 9. Project 3 - Text Summarization/2. Fetching article data from the web.srt 6.0 kB
  • 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.vtt 6.0 kB
  • 7. Project 1 - Text Classification/3. Importing the dataset.vtt 5.9 kB
  • 6. NLP Core/14. Building the BOW Model Part 3.srt 5.9 kB
  • 7. Project 1 - Text Classification/8. Creating training and test set.srt 5.8 kB
  • 7. Project 1 - Text Classification/4. Persisting the dataset.vtt 5.8 kB
  • 10. Word2Vec Analysis/2. Importing the data.vtt 5.7 kB
  • 6. NLP Core/12. Building the BOW Model Part 1.srt 5.6 kB
  • 9. Project 3 - Text Summarization/6. Building the histogram.srt 5.6 kB
  • 4. Regular Expressions/1. Introduction to Regular Expressions.vtt 5.5 kB
  • 6. NLP Core/2. Tokenizing Words and Sentences.srt 5.5 kB
  • 6. NLP Core/1. Installing NLTK in Python.srt 5.4 kB
  • 2. Getting the required softwares/3. A tour of Spyder IDE.vtt 5.4 kB
  • 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.srt 5.4 kB
  • 6. NLP Core/20. Building the TF-IDF Model Part 4.srt 5.4 kB
  • 6. NLP Core/13. Building the BOW Model Part 2.vtt 5.4 kB
  • 7. Project 1 - Text Classification/5. Preprocessing the data.vtt 5.4 kB
  • 9. Project 3 - Text Summarization/8. Getting the summary.vtt 5.3 kB
  • 9. Project 3 - Text Summarization/2. Fetching article data from the web.vtt 5.3 kB
  • 7. Project 1 - Text Classification/8. Creating training and test set.vtt 5.1 kB
  • 7. Project 1 - Text Classification/13. Importing and using our Model.srt 5.1 kB
  • 6. NLP Core/14. Building the BOW Model Part 3.vtt 5.1 kB
  • 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.srt 5.1 kB
  • 10. Word2Vec Analysis/5. Testing Model Performance.srt 5.1 kB
  • 6. NLP Core/12. Building the BOW Model Part 1.vtt 4.9 kB
  • 9. Project 3 - Text Summarization/6. Building the histogram.vtt 4.9 kB
  • 6. NLP Core/1. Installing NLTK in Python.vtt 4.8 kB
  • 6. NLP Core/2. Tokenizing Words and Sentences.vtt 4.8 kB
  • 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.vtt 4.7 kB
  • 6. NLP Core/20. Building the TF-IDF Model Part 4.vtt 4.7 kB
  • 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.srt 4.7 kB
  • 6. NLP Core/6. Lemmatization using NLTK.srt 4.6 kB
  • 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.srt 4.6 kB
  • 2. Getting the required softwares/1. Installing Anaconda Python.srt 4.6 kB
  • 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.vtt 4.4 kB
  • 7. Project 1 - Text Classification/13. Importing and using our Model.vtt 4.4 kB
  • 10. Word2Vec Analysis/5. Testing Model Performance.vtt 4.4 kB
  • 10. Word2Vec Analysis/3. Preparing the data.srt 4.2 kB
  • 9. Project 3 - Text Summarization/4. Preprocessing the data.srt 4.2 kB
  • 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.vtt 4.1 kB
  • 2. Getting the required softwares/1. Installing Anaconda Python.vtt 4.0 kB
  • 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.vtt 4.0 kB
  • 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.srt 4.0 kB
  • 6. NLP Core/6. Lemmatization using NLTK.vtt 3.9 kB
  • 10. Word2Vec Analysis/3. Preparing the data.vtt 3.7 kB
  • 9. Project 3 - Text Summarization/4. Preprocessing the data.vtt 3.6 kB
  • 10. Word2Vec Analysis/4. Training the Word2Vec Model.srt 3.6 kB
  • 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.vtt 3.4 kB
  • 6. NLP Core/9. POS Tag Meanings.html 3.4 kB
  • 10. Word2Vec Analysis/4. Training the Word2Vec Model.vtt 3.1 kB
  • 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.srt 2.6 kB
  • 7. Project 1 - Text Classification/10. Training our classifier.srt 2.4 kB
  • 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.srt 2.3 kB
  • 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.vtt 2.3 kB
  • 1. Introduction to the Course/2. Getting the Course Resources.srt 2.1 kB
  • 7. Project 1 - Text Classification/10. Training our classifier.vtt 2.1 kB
  • 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.vtt 2.1 kB
  • 1. Introduction to the Course/2. Getting the Course Resources.vtt 1.9 kB
  • 2. Getting the required softwares/4. How to take this course.html 1.7 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 Bytes
  • 2. Getting the required softwares/2. Installing Anaconda Python - Text.html 734 Bytes
  • 11. Conclusion/1. Where you go from here.html 727 Bytes
  • 1. Introduction to the Course/3. Getting the Course Resources - Text.html 614 Bytes
  • 3. Python Crash Course/12. Test Your Skills.html 156 Bytes
  • 4. Regular Expressions/8. Test Your Skills.html 156 Bytes
  • [FCS Forum].url 133 Bytes
  • [FreeCourseSite.com].url 127 Bytes
  • [CourseClub.NET].url 123 Bytes

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