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

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文件数目:268个文件
文件大小:7.99 GB
收录时间:2018-09-11
访问次数:5
相关内容:FreeTutorialsUdemyHandsNaturalLanguageProcessingusingPython
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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
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