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[] Udemy - Natural Language Processing - NLP In Python with Projects

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文件列表

  1. 6. Text Classification using ML/4. Implementing a Naive Bayes Classifier.mp4 142MB
  2. 2. Feature Engineering for NLP/3. Finding the Length, Polarity and Subjectivity.mp4 91MB
  3. 1. Introduction to NLP/6. Popular Libraries used for NLP.mp4 81MB
  4. 7. Sentiment analyzer/4. Cleaning the data.mp4 79MB
  5. 3. Data Cleaning for NLP/6. Stemming and Lemmatization.mp4 75MB
  6. 1. Introduction to NLP/5. Introduction to Text Processing.mp4 66MB
  7. 1. Introduction to NLP/2. Why should you learn NLP.mp4 62MB
  8. 1. Introduction to NLP/4. Steps to solve NLP Problems.mp4 60MB
  9. 1. Introduction to NLP/3. Applications of NLP.mp4 59MB
  10. 4. Feature Extraction for NLP/3. Introduction to TFIDF.mp4 59MB
  11. 7. Sentiment analyzer/3. Scraping Data from Social Media Websites.mp4 58MB
  12. 7. Sentiment analyzer/1. Setting up the Environment.mp4 57MB
  13. 8. Drugs Prescription using Reviews/7. Calculating Sentiment from Reviews.mp4 56MB
  14. 3. Data Cleaning for NLP/7. Quiz Solution.mp4 55MB
  15. 1. Introduction to NLP/7. Quiz Solution.mp4 51MB
  16. 8. Drugs Prescription using Reviews/10. Finding Most Useful and Useful Drugs for each Condition.mp4 50MB
  17. 1. Introduction to NLP/1. What is NLP.mp4 49MB
  18. 2. Feature Engineering for NLP/7. Quiz Solution.mp4 49MB
  19. 6. Text Classification using ML/7. Quiz Solution.mp4 49MB
  20. 6. Text Classification using ML/5. Implementing a SVM Classifier.mp4 48MB
  21. 8. Drugs Prescription using Reviews/6. Cleaning the Reviews.mp4 48MB
  22. 7. Sentiment analyzer/5. Creating a Sentiment Analyzer Engine.mp4 47MB
  23. 8. Drugs Prescription using Reviews/5. Unveiling Hidden Patterns from the Dataset.mp4 47MB
  24. 9. Outro Section/1. Conclusion.mp4 46MB
  25. 4. Feature Extraction for NLP/5. Introduction to N Grams Analysis.mp4 46MB
  26. 7. Sentiment analyzer/2. Understanding the problem statement.mp4 46MB
  27. 8. Drugs Prescription using Reviews/2. Understanding the Dataset.mp4 46MB
  28. 8. Drugs Prescription using Reviews/9. Analysing the Medical Conditions.mp4 45MB
  29. 4. Feature Extraction for NLP/6. Implementing N Grams Analysis.mp4 43MB
  30. 8. Drugs Prescription using Reviews/1. Setting up the Environment.mp4 43MB
  31. 3. Data Cleaning for NLP/5. Introduction to Stop words.mp4 43MB
  32. 4. Feature Extraction for NLP/7. Quiz Solution.mp4 43MB
  33. 6. Text Classification using ML/3. Best Models for Text Classification.mp4 42MB
  34. 5. Data Visualization for NLP/7. Quiz Solution.mp4 42MB
  35. 8. Drugs Prescription using Reviews/4. Summarizing the Dataset.mp4 41MB
  36. 4. Feature Extraction for NLP/2. Introduction to Bag of Words.mp4 41MB
  37. 3. Data Cleaning for NLP/3. Performing Tokenization.mp4 41MB
  38. 7. Sentiment analyzer/6. Visualizing results.mp4 41MB
  39. 8. Drugs Prescription using Reviews/8. Calculating Effectiveness and Usefulness of Drugs.mp4 40MB
  40. 6. Text Classification using ML/1. What is Text Classification.mp4 40MB
  41. 6. Text Classification using ML/2. Applications for Text Classification.mp4 39MB
  42. 8. Drugs Prescription using Reviews/3. Understanding the Problem Statement.mp4 39MB
  43. 5. Data Visualization for NLP/6. Introduction to Words Cloud.mp4 39MB
  44. 3. Data Cleaning for NLP/1. Why Is it so Necessary to Clean the Data.mp4 38MB
  45. 5. Data Visualization for NLP/3. Part-of-Speech Tagging.mp4 37MB
  46. 4. Feature Extraction for NLP/1. What is Feature Extraction.mp4 36MB
  47. 2. Feature Engineering for NLP/2. Reading and Summarizing the Text Data.mp4 36MB
  48. 2. Feature Engineering for NLP/4. Finding the Words, Characters, and Punctuation Count.mp4 35MB
  49. 3. Data Cleaning for NLP/4. Removing Special and accented Characters.mp4 34MB
  50. 5. Data Visualization for NLP/1. Importance of Data Visualization in NLP.mp4 33MB
  51. 4. Feature Extraction for NLP/4. Implementing bag of Words and TFIDF.mp4 31MB
  52. 7. Sentiment analyzer/7. Major Takeaways.mp4 29MB
  53. 2. Feature Engineering for NLP/1. Introduction to Feature Engineering.mp4 29MB
  54. 2. Feature Engineering for NLP/5. Counting Nouns and Verbs in the Text.mp4 28MB
  55. 5. Data Visualization for NLP/2. Visualizing Polarity and Subjectivity.mp4 28MB
  56. 3. Data Cleaning for NLP/2. Removing Punctuations and Numbers.mp4 27MB
  57. 5. Data Visualization for NLP/5. Visualizing N-Grams.mp4 26MB
  58. 6. Text Classification using ML/6. More Things to Try.mp4 25MB
  59. 2. Feature Engineering for NLP/6. Counting Adjectives, Adverb, and Pronouns.mp4 24MB
  60. 5. Data Visualization for NLP/4. Visualizing Most Frequent Words.mp4 23MB
  61. 6. Text Classification using ML/4. Implementing a Naive Bayes Classifier.srt 10KB
  62. 2. Feature Engineering for NLP/3. Finding the Length, Polarity and Subjectivity.srt 6KB
  63. 7. Sentiment analyzer/4. Cleaning the data.srt 5KB
  64. 8. Drugs Prescription using Reviews/4. Summarizing the Dataset.srt 5KB
  65. 3. Data Cleaning for NLP/6. Stemming and Lemmatization.srt 5KB
  66. 1. Introduction to NLP/6. Popular Libraries used for NLP.srt 5KB
  67. 8. Drugs Prescription using Reviews/7. Calculating Sentiment from Reviews.srt 4KB
  68. 2. Feature Engineering for NLP/4. Finding the Words, Characters, and Punctuation Count.srt 4KB
  69. 7. Sentiment analyzer/3. Scraping Data from Social Media Websites.srt 4KB
  70. 8. Drugs Prescription using Reviews/2. Understanding the Dataset.srt 4KB
  71. 1. Introduction to NLP/7. Quiz Solution.srt 4KB
  72. 3. Data Cleaning for NLP/7. Quiz Solution.srt 4KB
  73. 6. Text Classification using ML/5. Implementing a SVM Classifier.srt 4KB
  74. 8. Drugs Prescription using Reviews/9. Analysing the Medical Conditions.srt 4KB
  75. 8. Drugs Prescription using Reviews/1. Setting up the Environment.srt 4KB
  76. 8. Drugs Prescription using Reviews/5. Unveiling Hidden Patterns from the Dataset.srt 4KB
  77. 1. Introduction to NLP/5. Introduction to Text Processing.srt 4KB
  78. 2. Feature Engineering for NLP/7. Quiz Solution.srt 4KB
  79. 1. Introduction to NLP/2. Why should you learn NLP.srt 4KB
  80. 3. Data Cleaning for NLP/5. Introduction to Stop words.srt 4KB
  81. 7. Sentiment analyzer/5. Creating a Sentiment Analyzer Engine.srt 4KB
  82. 5. Data Visualization for NLP/3. Part-of-Speech Tagging.srt 3KB
  83. 3. Data Cleaning for NLP/3. Performing Tokenization.srt 3KB
  84. 6. Text Classification using ML/7. Quiz Solution.srt 3KB
  85. 1. Introduction to NLP/4. Steps to solve NLP Problems.srt 3KB
  86. 2. Feature Engineering for NLP/2. Reading and Summarizing the Text Data.srt 3KB
  87. 8. Drugs Prescription using Reviews/8. Calculating Effectiveness and Usefulness of Drugs.srt 3KB
  88. 5. Data Visualization for NLP/7. Quiz Solution.srt 3KB
  89. 4. Feature Extraction for NLP/7. Quiz Solution.srt 3KB
  90. 1. Introduction to NLP/3. Applications of NLP.srt 3KB
  91. 8. Drugs Prescription using Reviews/6. Cleaning the Reviews.srt 3KB
  92. 7. Sentiment analyzer/1. Setting up the Environment.srt 3KB
  93. 8. Drugs Prescription using Reviews/10. Finding Most Useful and Useful Drugs for each Condition.srt 3KB
  94. 4. Feature Extraction for NLP/3. Introduction to TFIDF.srt 3KB
  95. 4. Feature Extraction for NLP/6. Implementing N Grams Analysis.srt 3KB
  96. 9. Outro Section/1. Conclusion.srt 3KB
  97. 1. Introduction to NLP/1. What is NLP.srt 3KB
  98. 5. Data Visualization for NLP/2. Visualizing Polarity and Subjectivity.srt 3KB
  99. 2. Feature Engineering for NLP/5. Counting Nouns and Verbs in the Text.srt 3KB
  100. 7. Sentiment analyzer/2. Understanding the problem statement.srt 3KB
  101. 4. Feature Extraction for NLP/4. Implementing bag of Words and TFIDF.srt 3KB
  102. 7. Sentiment analyzer/6. Visualizing results.srt 2KB
  103. 5. Data Visualization for NLP/6. Introduction to Words Cloud.srt 2KB
  104. 3. Data Cleaning for NLP/4. Removing Special and accented Characters.srt 2KB
  105. 6. Text Classification using ML/3. Best Models for Text Classification.srt 2KB
  106. 3. Data Cleaning for NLP/2. Removing Punctuations and Numbers.srt 2KB
  107. 4. Feature Extraction for NLP/5. Introduction to N Grams Analysis.srt 2KB
  108. 4. Feature Extraction for NLP/2. Introduction to Bag of Words.srt 2KB
  109. 6. Text Classification using ML/1. What is Text Classification.srt 2KB
  110. 6. Text Classification using ML/2. Applications for Text Classification.srt 2KB
  111. 5. Data Visualization for NLP/5. Visualizing N-Grams.srt 2KB
  112. 2. Feature Engineering for NLP/6. Counting Adjectives, Adverb, and Pronouns.srt 2KB
  113. 8. Drugs Prescription using Reviews/3. Understanding the Problem Statement.srt 2KB
  114. 3. Data Cleaning for NLP/1. Why Is it so Necessary to Clean the Data.srt 2KB
  115. 4. Feature Extraction for NLP/1. What is Feature Extraction.srt 2KB
  116. 2. Feature Engineering for NLP/1. Introduction to Feature Engineering.srt 2KB
  117. 5. Data Visualization for NLP/4. Visualizing Most Frequent Words.srt 2KB
  118. 5. Data Visualization for NLP/1. Importance of Data Visualization in NLP.srt 2KB
  119. 7. Sentiment analyzer/7. Major Takeaways.srt 1KB
  120. 6. Text Classification using ML/6. More Things to Try.srt 1KB
  121. 0. Websites you may like/[FCS Forum].url 133B
  122. 0. Websites you may like/[FreeCourseSite.com].url 127B
  123. 0. Websites you may like/[CourseClub.ME].url 122B
  124. 0. Websites you may like/[GigaCourse.Com].url 49B