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

Creation Time	Aug. 28, 2023, 9:30 a.m.
Last Access Time	April 29, 2026, 2:20 a.m.
File Size	8.0 GB
Keywords	On Natural Language FreeTutorials NLP Python Processing - Us Udemy Hands using
Total Requests	606
Total Files	268
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6. NLP Core/25. LSA in Python Part 1.mp4 295.6 MB
5. Numpy and Pandas/1. Introduction to Numpy.mp4 280.7 MB
6. NLP Core/21. Understanding the N-Gram Model.mp4 259.2 MB
5. Numpy and Pandas/2. Introduction to Pandas.mp4 251.6 MB
6. NLP Core/16. Text Modelling using TF-IDF Model.mp4 223.0 MB
7. Project 1 - Text Classification/9. Understanding Logistic Regression.mp4 201.6 MB
6. NLP Core/24. Understanding Latent Semantic Analysis.mp4 194.5 MB
6. NLP Core/26. LSA in Python Part 2.mp4 190.2 MB
6. NLP Core/22. Building Character N-Gram Model.mp4 185.7 MB
4. Regular Expressions/5. Shorthand Character Classes.mp4 182.4 MB
3. Python Crash Course/11. List Comprehension.mp4 165.5 MB
10. Word2Vec Analysis/1. Understanding Word Vectors.mp4 160.6 MB
6. NLP Core/23. Building Word N-Gram Model.mp4 160.5 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 133.5 MB
8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.mp4 133.1 MB
3. Python Crash Course/5. Python Data Structures - Lists.mp4 129.2 MB
3. Python Crash Course/7. Python Data Structures - Dictionaries.mp4 125.1 MB
6. NLP Core/18. Building the TF-IDF Model Part 2.mp4 122.7 MB
6. NLP Core/27. Word Synonyms and Antonyms using NLTK.mp4 118.0 MB
7. Project 1 - Text Classification/6. Transforming data into BOW Model.mp4 114.7 MB
6. NLP Core/17. Building the TF-IDF Model Part 1.mp4 109.9 MB
6. NLP Core/19. Building the TF-IDF Model Part 3.mp4 109.8 MB
6. NLP Core/8. Parts Of Speech Tagging.mp4 109.1 MB
10. Word2Vec Analysis/6. Improving the Model.mp4 108.2 MB
6. NLP Core/15. Building the BOW Model Part 4.mp4 108.1 MB
6. NLP Core/4. Introduction to Stemming and Lemmatization.mp4 107.5 MB
8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.mp4 102.7 MB
9. Project 3 - Text Summarization/7. Calculating the sentence scores.mp4 99.8 MB
3. Python Crash Course/8. Console and File IO in Python.mp4 97.0 MB
7. Project 1 - Text Classification/12. Saving our Model.mp4 96.6 MB
9. Project 3 - Text Summarization/1. Understanding Text Summarization.mp4 95.7 MB
9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.mp4 94.3 MB
3. Python Crash Course/10. Introduction to Classes and Objects.mp4 92.4 MB
6. NLP Core/28. Word Negation Tracking in Python Part 1.mp4 90.7 MB
6. NLP Core/12. Building the BOW Model Part 1.mp4 88.6 MB
7. Project 1 - Text Classification/11. Testing Model performance.mp4 84.0 MB
6. NLP Core/13. Building the BOW Model Part 2.mp4 82.2 MB
4. Regular Expressions/3. Finding Patterns in Text Part 2.mp4 81.5 MB
8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.mp4 80.9 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 77.0 MB
9. Project 3 - Text Summarization/8. Getting the summary.mp4 76.9 MB
3. Python Crash Course/9. Introduction to Functions.mp4 76.8 MB
6. NLP Core/6. Lemmatization using NLTK.mp4 76.5 MB
1. Introduction to the Course/1. What is NLP.mp4 75.7 MB
6. NLP Core/2. Tokenizing Words and Sentences.mp4 74.6 MB
7. Project 1 - Text Classification/8. Creating training and test set.mp4 71.8 MB
4. Regular Expressions/7. Preprocessing using Regex.mp4 71.6 MB
7. Project 1 - Text Classification/4. Persisting the dataset.mp4 71.6 MB
7. Project 1 - Text Classification/5. Preprocessing the data.mp4 67.4 MB
3. Python Crash Course/3. Introduction to Loops.mp4 64.8 MB
6. NLP Core/20. Building the TF-IDF Model Part 4.mp4 64.6 MB
3. Python Crash Course/2. Conditional Statements.mp4 63.8 MB
4. Regular Expressions/1. Introduction to Regular Expressions.mp4 62.8 MB
7. Project 1 - Text Classification/1. Getting the data for Text Classification.mp4 62.1 MB
3. Python Crash Course/4. Loop Control Statements.mp4 62.0 MB
3. Python Crash Course/6. Python Data Structures - Tuples.mp4 60.9 MB
3. Python Crash Course/1. Variables and Operations in Python.mp4 60.3 MB
6. NLP Core/29. Word Negation Tracking in Python Part 2.mp4 58.6 MB
9. Project 3 - Text Summarization/6. Building the histogram.mp4 58.6 MB
7. Project 1 - Text Classification/3. Importing the dataset.mp4 57.5 MB
7. Project 1 - Text Classification/13. Importing and using our Model.mp4 56.1 MB
6. NLP Core/10. Named Entity Recognition.mp4 56.1 MB
10. Word2Vec Analysis/2. Importing the data.mp4 54.9 MB
10. Word2Vec Analysis/5. Testing Model Performance.mp4 54.5 MB
4. Regular Expressions/4. Substituting Patterns in Text.mp4 54.2 MB
9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.mp4 50.7 MB
10. Word2Vec Analysis/7. Exploring Pre-trained Models.mp4 50.4 MB
9. Project 3 - Text Summarization/4. Preprocessing the data.mp4 48.3 MB
7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.mp4 47.4 MB
2. Getting the required softwares/3. A tour of Spyder IDE.mp4 46.8 MB
8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.mp4 46.7 MB
9. Project 3 - Text Summarization/2. Fetching article data from the web.mp4 43.9 MB
10. Word2Vec Analysis/3. Preparing the data.mp4 38.5 MB
8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.mp4 38.1 MB
8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.mp4 36.0 MB
8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.mp4 35.1 MB
10. Word2Vec Analysis/4. Training the Word2Vec Model.mp4 33.8 MB
2. Getting the required softwares/1. Installing Anaconda Python.mp4 33.4 MB
7. Project 1 - Text Classification/10. Training our classifier.mp4 30.7 MB
6. NLP Core/1. Installing NLTK in Python.mp4 29.3 MB
8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.mp4 28.3 MB
1. Introduction to the Course/2. Getting the Course Resources.mp4 18.2 MB
5. Numpy and Pandas/2. Introduction to Pandas.srt 28.6 KB
5. Numpy and Pandas/1. Introduction to Numpy.srt 27.1 KB
6. NLP Core/21. Understanding the N-Gram Model.srt 27.1 KB
6. NLP Core/25. LSA in Python Part 1.srt 25.9 KB
5. Numpy and Pandas/2. Introduction to Pandas.vtt 24.7 KB
6. NLP Core/21. Understanding the N-Gram Model.vtt 23.5 KB
5. Numpy and Pandas/1. Introduction to Numpy.vtt 23.5 KB
6. NLP Core/25. LSA in Python Part 1.vtt 22.3 KB
6. NLP Core/16. Text Modelling using TF-IDF Model.srt 22.1 KB
7. Project 1 - Text Classification/9. Understanding Logistic Regression.srt 20.4 KB
6. NLP Core/22. Building Character N-Gram Model.srt 20.2 KB
6. NLP Core/24. Understanding Latent Semantic Analysis.srt 19.3 KB
6. NLP Core/16. Text Modelling using TF-IDF Model.vtt 19.2 KB
7. Project 1 - Text Classification/9. Understanding Logistic Regression.vtt 17.9 KB
6. NLP Core/22. Building Character N-Gram Model.vtt 17.6 KB
4. Regular Expressions/5. Shorthand Character Classes.srt 17.3 KB
6. NLP Core/24. Understanding Latent Semantic Analysis.vtt 16.8 KB
3. Python Crash Course/11. List Comprehension.srt 16.6 KB
3. Python Crash Course/5. Python Data Structures - Lists.srt 16.0 KB
10. Word2Vec Analysis/1. Understanding Word Vectors.srt 16.0 KB
4. Regular Expressions/5. Shorthand Character Classes.vtt 15.0 KB
6. NLP Core/26. LSA in Python Part 2.srt 14.9 KB
6. NLP Core/23. Building Word N-Gram Model.srt 14.8 KB
6. NLP Core/11. Text Modelling using Bag of Words Model.srt 14.7 KB
3. Python Crash Course/11. List Comprehension.vtt 14.3 KB
3. Python Crash Course/7. Python Data Structures - Dictionaries.srt 14.2 KB
10. Word2Vec Analysis/1. Understanding Word Vectors.vtt 14.0 KB
3. Python Crash Course/5. Python Data Structures - Lists.vtt 13.9 KB
6. NLP Core/27. Word Synonyms and Antonyms using NLTK.srt 13.2 KB
6. NLP Core/26. LSA in Python Part 2.vtt 12.9 KB
6. NLP Core/23. Building Word N-Gram Model.vtt 12.9 KB
6. NLP Core/11. Text Modelling using Bag of Words Model.vtt 12.8 KB
6. NLP Core/28. Word Negation Tracking in Python Part 1.srt 12.7 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 11.4 KB
6. NLP Core/28. Word Negation Tracking in Python Part 1.vtt 11.0 KB
4. Regular Expressions/2. Finding Patterns in Text Part 1.srt 10.9 KB
6. NLP Core/4. Introduction to Stemming and Lemmatization.srt 10.1 KB
4. Regular Expressions/3. Finding Patterns in Text Part 2.srt 9.9 KB
3. Python Crash Course/3. Introduction to Loops.srt 9.8 KB
9. Project 3 - Text Summarization/1. Understanding Text Summarization.srt 9.8 KB
7. Project 1 - Text Classification/6. Transforming data into BOW Model.srt 9.7 KB
3. Python Crash Course/8. Console and File IO in Python.srt 9.7 KB
3. Python Crash Course/1. Variables and Operations in Python.srt 9.5 KB
4. Regular Expressions/2. Finding Patterns in Text Part 1.vtt 9.5 KB
9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.srt 9.5 KB
6. NLP Core/18. Building the TF-IDF Model Part 2.srt 9.4 KB
3. Python Crash Course/10. Introduction to Classes and Objects.srt 9.4 KB
3. Python Crash Course/4. Loop Control Statements.srt 9.4 KB
6. NLP Core/4. Introduction to Stemming and Lemmatization.vtt 8.8 KB
8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.srt 8.7 KB
4. Regular Expressions/3. Finding Patterns in Text Part 2.vtt 8.6 KB
6. NLP Core/7. Stop word removal using NLTK.srt 8.6 KB
3. Python Crash Course/3. Introduction to Loops.vtt 8.6 KB
7. Project 1 - Text Classification/6. Transforming data into BOW Model.vtt 8.6 KB
9. Project 3 - Text Summarization/1. Understanding Text Summarization.vtt 8.5 KB
6. NLP Core/5. Stemming using NLTK.srt 8.5 KB
6. NLP Core/15. Building the BOW Model Part 4.srt 8.4 KB
3. Python Crash Course/8. Console and File IO in Python.vtt 8.4 KB
6. NLP Core/19. Building the TF-IDF Model Part 3.srt 8.4 KB
3. Python Crash Course/9. Introduction to Functions.srt 8.3 KB
3. Python Crash Course/1. Variables and Operations in Python.vtt 8.3 KB
6. NLP Core/18. Building the TF-IDF Model Part 2.vtt 8.2 KB
3. Python Crash Course/10. Introduction to Classes and Objects.vtt 8.2 KB
6. NLP Core/17. Building the TF-IDF Model Part 1.srt 8.2 KB
9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.vtt 8.2 KB
3. Python Crash Course/4. Loop Control Statements.vtt 8.2 KB
6. NLP Core/29. Word Negation Tracking in Python Part 2.srt 8.1 KB
4. Regular Expressions/4. Substituting Patterns in Text.srt 8.1 KB
4. Regular Expressions/7. Preprocessing using Regex.srt 8.0 KB
9. Project 3 - Text Summarization/7. Calculating the sentence scores.srt 7.9 KB
10. Word2Vec Analysis/6. Improving the Model.srt 7.8 KB
6. NLP Core/8. Parts Of Speech Tagging.srt 7.8 KB
7. Project 1 - Text Classification/12. Saving our Model.srt 7.8 KB
1. Introduction to the Course/1. What is NLP.srt 7.7 KB
7. Project 1 - Text Classification/1. Getting the data for Text Classification.srt 7.6 KB
8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.vtt 7.6 KB
6. NLP Core/7. Stop word removal using NLTK.vtt 7.5 KB
6. NLP Core/5. Stemming using NLTK.vtt 7.4 KB
6. NLP Core/15. Building the BOW Model Part 4.vtt 7.4 KB
6. NLP Core/19. Building the TF-IDF Model Part 3.vtt 7.3 KB
3. Python Crash Course/9. Introduction to Functions.vtt 7.2 KB
6. NLP Core/17. Building the TF-IDF Model Part 1.vtt 7.2 KB
7. Project 1 - Text Classification/11. Testing Model performance.srt 7.2 KB
3. Python Crash Course/6. Python Data Structures - Tuples.srt 7.1 KB
6. NLP Core/29. Word Negation Tracking in Python Part 2.vtt 7.1 KB
8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.srt 7.0 KB
4. Regular Expressions/4. Substituting Patterns in Text.vtt 7.0 KB
3. Python Crash Course/2. Conditional Statements.srt 7.0 KB
9. Project 3 - Text Summarization/7. Calculating the sentence scores.vtt 7.0 KB
4. Regular Expressions/7. Preprocessing using Regex.vtt 6.9 KB
6. NLP Core/10. Named Entity Recognition.srt 6.8 KB
7. Project 1 - Text Classification/12. Saving our Model.vtt 6.8 KB
6. NLP Core/8. Parts Of Speech Tagging.vtt 6.8 KB
10. Word2Vec Analysis/7. Exploring Pre-trained Models.srt 6.7 KB
8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.srt 6.7 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 6.7 KB
1. Introduction to the Course/1. What is NLP.vtt 6.7 KB
7. Project 1 - Text Classification/3. Importing the dataset.srt 6.6 KB
10. Word2Vec Analysis/2. Importing the data.srt 6.5 KB
7. Project 1 - Text Classification/4. Persisting the dataset.srt 6.5 KB
7. Project 1 - Text Classification/11. Testing Model performance.vtt 6.2 KB
4. Regular Expressions/1. Introduction to Regular Expressions.srt 6.1 KB
3. Python Crash Course/6. Python Data Structures - Tuples.vtt 6.1 KB
2. Getting the required softwares/3. A tour of Spyder IDE.srt 6.1 KB
3. Python Crash Course/2. Conditional Statements.vtt 6.1 KB
8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.vtt 6.0 KB
7. Project 1 - Text Classification/5. Preprocessing the data.srt 6.0 KB
6. NLP Core/13. Building the BOW Model Part 2.srt 6.0 KB
6. NLP Core/10. Named Entity Recognition.vtt 6.0 KB
9. Project 3 - Text Summarization/8. Getting the summary.srt 6.0 KB
10. Word2Vec Analysis/7. Exploring Pre-trained Models.vtt 5.9 KB
9. Project 3 - Text Summarization/2. Fetching article data from the web.srt 5.9 KB
8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.vtt 5.9 KB
7. Project 1 - Text Classification/3. Importing the dataset.vtt 5.8 KB
6. NLP Core/14. Building the BOW Model Part 3.srt 5.7 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 5.7 KB
10. Word2Vec Analysis/2. Importing the data.vtt 5.6 KB
6. NLP Core/12. Building the BOW Model Part 1.srt 5.4 KB
9. Project 3 - Text Summarization/6. Building the histogram.srt 5.4 KB
4. Regular Expressions/1. Introduction to Regular Expressions.vtt 5.4 KB
6. NLP Core/2. Tokenizing Words and Sentences.srt 5.3 KB
6. NLP Core/1. Installing NLTK in Python.srt 5.3 KB
2. Getting the required softwares/3. A tour of Spyder IDE.vtt 5.3 KB
8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.srt 5.3 KB
6. NLP Core/20. Building the TF-IDF Model Part 4.srt 5.3 KB
6. NLP Core/13. Building the BOW Model Part 2.vtt 5.3 KB
7. Project 1 - Text Classification/5. Preprocessing the data.vtt 5.2 KB
9. Project 3 - Text Summarization/8. Getting the summary.vtt 5.2 KB
9. Project 3 - Text Summarization/2. Fetching article data from the web.vtt 5.1 KB
7. Project 1 - Text Classification/8. Creating training and test set.vtt 5.0 KB
7. Project 1 - Text Classification/13. Importing and using our Model.srt 5.0 KB
6. NLP Core/14. Building the BOW Model Part 3.vtt 5.0 KB
8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.srt 5.0 KB
10. Word2Vec Analysis/5. Testing Model Performance.srt 4.9 KB
6. NLP Core/12. Building the BOW Model Part 1.vtt 4.8 KB
9. Project 3 - Text Summarization/6. Building the histogram.vtt 4.7 KB
6. NLP Core/1. Installing NLTK in Python.vtt 4.7 KB
6. NLP Core/2. Tokenizing Words and Sentences.vtt 4.7 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 4.6 KB
8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.srt 4.6 KB
6. NLP Core/6. Lemmatization using NLTK.srt 4.5 KB
9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.srt 4.5 KB
2. Getting the required softwares/1. Installing Anaconda Python.srt 4.5 KB
8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.vtt 4.3 KB
7. Project 1 - Text Classification/13. Importing and using our Model.vtt 4.3 KB
10. Word2Vec Analysis/5. Testing Model Performance.vtt 4.3 KB
10. Word2Vec Analysis/3. Preparing the data.srt 4.1 KB
9. Project 3 - Text Summarization/4. Preprocessing the data.srt 4.1 KB
8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.vtt 4.0 KB
2. Getting the required softwares/1. Installing Anaconda Python.vtt 3.9 KB
9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.vtt 3.9 KB
7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.srt 3.9 KB
6. NLP Core/6. Lemmatization using NLTK.vtt 3.9 KB
10. Word2Vec Analysis/3. Preparing the data.vtt 3.6 KB
9. Project 3 - Text Summarization/4. Preprocessing the data.vtt 3.6 KB
10. Word2Vec Analysis/4. Training the Word2Vec Model.srt 3.5 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 3.3 KB
10. Word2Vec Analysis/4. Training the Word2Vec Model.vtt 3.0 KB
8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.srt 2.5 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 2.3 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 2.1 KB
7. Project 1 - Text Classification/10. Training our classifier.vtt 2.0 KB
8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.vtt 2.0 KB
1. Introduction to the Course/2. Getting the Course Resources.vtt 1.8 KB
2. Getting the required softwares/4. How to take this course.html 1.6 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
[FTU Forum].url 252 bytes
3. Python Crash Course/12. Test Your Skills.html 156 bytes
4. Regular Expressions/8. Test Your Skills.html 156 bytes
[FreeCoursesOnline.Me].url 133 bytes
[FreeTutorials.Us].url 119 bytes

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

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