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[Udemy] Natural Language Processing (NLP) in Python with 8 Projects ()

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  • 文件大小:5GB
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文件列表

  1. 09 - Deep Learning Basics/002 Activation Function.mp4 157MB
  2. 10 - Word Embeddings/001 Introduction to Word Embedding.mp4 146MB
  3. 01 - Welcome/003 Introduction to NLP.mp4 134MB
  4. 13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/001 Text Generation Part I.mp4 114MB
  5. 14 - FastText Library for Text Classification/006 Text Classification with Fasttext.mp4 106MB
  6. 09 - Deep Learning Basics/001 The Neuron.mp4 102MB
  7. 11 - Project 6 _ Text Classification with CNN/001 Convolutional Neural Network Part 1.mp4 96MB
  8. 11 - Project 6 _ Text Classification with CNN/003 Spam Detection with CNN - I.mp4 91MB
  9. 17 - Data Visualization with Matplotlib/006 Matplotlib Part 4.mp4 91MB
  10. 17 - Data Visualization with Matplotlib/001 Matplotlib Part 1 - Functional Method.mp4 91MB
  11. 03 - Basics of Natural Language Processing/008 Vocabulary and Matching Part - 1.mp4 84MB
  12. 03 - Basics of Natural Language Processing/012 Named Entity Recognition.mp4 83MB
  13. 11 - Project 6 _ Text Classification with CNN/002 Convolutional Neural Network Part 2.mp4 81MB
  14. 02 - Installation & Setup/001 Course Installation.mp4 81MB
  15. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/001 Review Classification Part -1.mp4 79MB
  16. 16 - Data analysis with Pandas/003 DataFrames Part 1.mp4 78MB
  17. 11 - Project 6 _ Text Classification with CNN/004 Spam Detection with CNN - II.mp4 78MB
  18. 09 - Deep Learning Basics/004 Gradient Descent and Back-Propagation.mp4 75MB
  19. 08 - Project 5 _ Twitter sentiment Analysis/003 Find Setiment from Tweets.mp4 74MB
  20. 03 - Basics of Natural Language Processing/002 Tokenization Basic Part - 1.mp4 73MB
  21. 03 - Basics of Natural Language Processing/009 Vocabulary and Matching Part - 2 (Rule Based).mp4 73MB
  22. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/002 Review Classification Part - 2.mp4 72MB
  23. 10 - Word Embeddings/002 Train Model for Embedding - I.mp4 71MB
  24. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/004 Bag of Word Model.mp4 70MB
  25. 16 - Data analysis with Pandas/002 Pandas Series.mp4 70MB
  26. 12 - Project 7 _ Text Classification with RNN/004 Spam Detection with RNN.mp4 64MB
  27. 04 - Project 1 _ Spam Message Classification/004 Apply Random Forest.mp4 64MB
  28. 10 - Word Embeddings/004 Embeddings with Pretrained model.mp4 64MB
  29. 07 - Project 4 _ Automated Text Summarization/001 Importing the libraries and Dataset.mp4 61MB
  30. 12 - Project 7 _ Text Classification with RNN/003 LSTM and GRU.mp4 60MB
  31. 18 - Appendix/002 Text File Processing - II.mp4 58MB
  32. 07 - Project 4 _ Automated Text Summarization/003 Calculate Sentence Score.mp4 57MB
  33. 16 - Data analysis with Pandas/008 Merging, Joining and Concatenating DataFrames.mp4 57MB
  34. 16 - Data analysis with Pandas/005 DataFrames Part 3.mp4 56MB
  35. 03 - Basics of Natural Language Processing/011 Parts of Speech Tagging.mp4 56MB
  36. 16 - Data analysis with Pandas/004 DataFrames Part 2.mp4 55MB
  37. 18 - Appendix/003 Text File Processing - III.mp4 55MB
  38. 15 - Data analysis with Numpy/003 Numpy Arrays Part 2.mp4 54MB
  39. 17 - Data Visualization with Matplotlib/004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI.mp4 54MB
  40. 03 - Basics of Natural Language Processing/013 Sentence Segmentation.mp4 53MB
  41. 09 - Deep Learning Basics/003 Cost Function.mp4 52MB
  42. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/003 Cleaning Text Data with NLTK - 2.mp4 51MB
  43. 10 - Word Embeddings/003 Train Model for Embedding - II.mp4 50MB
  44. 03 - Basics of Natural Language Processing/003 Tokenization Basic Part - 2.mp4 50MB
  45. 04 - Project 1 _ Spam Message Classification/002 Data Exploration & Preprocessing.mp4 50MB
  46. 17 - Data Visualization with Matplotlib/005 Matplotlib Part 3.mp4 50MB
  47. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/002 Cleaning Text Data with NLTK - 1.mp4 50MB
  48. 08 - Project 5 _ Twitter sentiment Analysis/001 Setting up Twitter Developer application.mp4 49MB
  49. 16 - Data analysis with Pandas/007 Groupby Method.mp4 49MB
  50. 03 - Basics of Natural Language Processing/001 Section _ Introduction.mp4 49MB
  51. 13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/002 Text Generation Part II.mp4 47MB
  52. 16 - Data analysis with Pandas/010 Reading and Writing Files in Pandas.mp4 46MB
  53. 14 - FastText Library for Text Classification/004 Create Linux Virtual Machine.mp4 46MB
  54. 18 - Appendix/005 Working with PDF File - I.mp4 46MB
  55. 15 - Data analysis with Numpy/005 Numpy Indexing and Selection Part 1.mp4 45MB
  56. 17 - Data Visualization with Matplotlib/002 Matplotlib Part 1 - Object Oriented Method.mp4 44MB
  57. 14 - FastText Library for Text Classification/005 Install fasttext library.mp4 43MB
  58. 04 - Project 1 _ Spam Message Classification/001 Business Problem & Dataset.mp4 43MB
  59. 07 - Project 4 _ Automated Text Summarization/002 Create Word Frequency Counter.mp4 42MB
  60. 04 - Project 1 _ Spam Message Classification/003 Split Data in Training & Testing.mp4 40MB
  61. 18 - Appendix/001 Text File Processing - I.mp4 40MB
  62. 12 - Project 7 _ Text Classification with RNN/001 Introduction to Recurrent Neural Networks.mp4 40MB
  63. 12 - Project 7 _ Text Classification with RNN/002 Vanishing Gradient Problem.mp4 39MB
  64. 16 - Data analysis with Pandas/009 Pandas Operations.mp4 39MB
  65. 17 - Data Visualization with Matplotlib/003 Matplotlib Part 2 - Subplots Method.mp4 38MB
  66. 01 - Welcome/001 Course Overview.mp4 35MB
  67. 16 - Data analysis with Pandas/006 Missing Data.mp4 35MB
  68. 04 - Project 1 _ Spam Message Classification/005 Apply Support vector Machine (SVM).mp4 34MB
  69. 03 - Basics of Natural Language Processing/010 Vocabulary and Matching Part - 3 (Phrase Based).mp4 33MB
  70. 03 - Basics of Natural Language Processing/007 Stop Words.mp4 33MB
  71. 14 - FastText Library for Text Classification/003 Virtual Box Installation.mp4 32MB
  72. 08 - Project 5 _ Twitter sentiment Analysis/002 Fetch Tweet from Tweeter server.mp4 31MB
  73. 15 - Data analysis with Numpy/007 Numpy Operations.mp4 29MB
  74. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/005 Apply Naive Bayes Algorithm.mp4 29MB
  75. 15 - Data analysis with Numpy/004 Numpy Arrays Part 3.mp4 27MB
  76. 07 - Project 4 _ Automated Text Summarization/004 Extract summary of document.mp4 27MB
  77. 03 - Basics of Natural Language Processing/005 Stemming & Lemmatization - 1.mp4 27MB
  78. 15 - Data analysis with Numpy/006 Numpy Indexing and Selection Part 2.mp4 27MB
  79. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/001 Business Problem.mp4 26MB
  80. 03 - Basics of Natural Language Processing/006 Stemming & Lemmatization - 2.mp4 23MB
  81. 15 - Data analysis with Numpy/002 Numpy Arrays Part 1.mp4 17MB
  82. 15 - Data analysis with Numpy/001 Introduction to NumPy.mp4 16MB
  83. 04 - Project 1 _ Spam Message Classification/006 Predict Testing Data both model.mp4 16MB
  84. 18 - Appendix/004 Text File Processing - IV.mp4 15MB
  85. 03 - Basics of Natural Language Processing/004 Tokenization Basic Part - 3.mp4 13MB
  86. 16 - Data analysis with Pandas/001 Pandas Introduction.mp4 13MB
  87. 14 - FastText Library for Text Classification/001 fasttext Installation steps [Video].mp4 8MB
  88. 01 - Welcome/002 Reviews UPDATE.mp4 5MB
  89. 04 - Project 1 _ Spam Message Classification/25152746-spam.tsv 502KB
  90. 11 - Project 6 _ Text Classification with CNN/25153370-spam.csv 492KB
  91. 12 - Project 7 _ Text Classification with RNN/25153382-spam.csv 492KB
  92. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152804-imdb-labelled.txt 83KB
  93. 14 - FastText Library for Text Classification/27130276-reviews.txt 70KB
  94. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/25152756-Restaurant-Reviews.tsv 60KB
  95. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152808-yelp-labelled.txt 60KB
  96. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/25152800-amazon-cells-labelled.txt 57KB
  97. 14 - FastText Library for Text Classification/006 Text Classification with Fasttext_en.vtt 16KB
  98. 03 - Basics of Natural Language Processing/012 Named Entity Recognition_en.vtt 13KB
  99. 13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/001 Text Generation Part I_en.vtt 13KB
  100. 02 - Installation & Setup/001 Course Installation_en.vtt 12KB
  101. 04 - Project 1 _ Spam Message Classification/004 Apply Random Forest_en.vtt 12KB
  102. 10 - Word Embeddings/001 Introduction to Word Embedding_en.vtt 12KB
  103. 18 - Appendix/003 Text File Processing - III_en.vtt 11KB
  104. 16 - Data analysis with Pandas/003 DataFrames Part 1_en.vtt 11KB
  105. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/001 Review Classification Part -1_en.vtt 11KB
  106. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/004 Bag of Word Model_en.vtt 10KB
  107. 11 - Project 6 _ Text Classification with CNN/003 Spam Detection with CNN - I_en.vtt 10KB
  108. 08 - Project 5 _ Twitter sentiment Analysis/003 Find Setiment from Tweets_en.vtt 10KB
  109. 16 - Data analysis with Pandas/002 Pandas Series_en.vtt 10KB
  110. 17 - Data Visualization with Matplotlib/001 Matplotlib Part 1 - Functional Method_en.vtt 10KB
  111. 03 - Basics of Natural Language Processing/002 Tokenization Basic Part - 1_en.vtt 10KB
  112. 03 - Basics of Natural Language Processing/008 Vocabulary and Matching Part - 1_en.vtt 10KB
  113. 11 - Project 6 _ Text Classification with CNN/004 Spam Detection with CNN - II_en.vtt 10KB
  114. 04 - Project 1 _ Spam Message Classification/002 Data Exploration & Preprocessing_en.vtt 10KB
  115. 16 - Data analysis with Pandas/004 DataFrames Part 2_en.vtt 9KB
  116. 10 - Word Embeddings/002 Train Model for Embedding - I_en.vtt 9KB
  117. 06 - Project 3 _ IMDB, Amazon and Yelp review Classification/002 Review Classification Part - 2_en.vtt 9KB
  118. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/002 Cleaning Text Data with NLTK - 1_en.vtt 9KB
  119. 03 - Basics of Natural Language Processing/013 Sentence Segmentation_en.vtt 9KB
  120. 15 - Data analysis with Numpy/003 Numpy Arrays Part 2_en.vtt 9KB
  121. 16 - Data analysis with Pandas/005 DataFrames Part 3_en.vtt 9KB
  122. 03 - Basics of Natural Language Processing/009 Vocabulary and Matching Part - 2 (Rule Based)_en.vtt 9KB
  123. 14 - FastText Library for Text Classification/004 Create Linux Virtual Machine_en.vtt 9KB
  124. 17 - Data Visualization with Matplotlib/006 Matplotlib Part 4_en.vtt 9KB
  125. 03 - Basics of Natural Language Processing/003 Tokenization Basic Part - 2_en.vtt 8KB
  126. 09 - Deep Learning Basics/002 Activation Function_en.vtt 8KB
  127. 18 - Appendix/005 Working with PDF File - I_en.vtt 8KB
  128. 18 - Appendix/002 Text File Processing - II_en.vtt 8KB
  129. 03 - Basics of Natural Language Processing/011 Parts of Speech Tagging_en.vtt 8KB
  130. 04 - Project 1 _ Spam Message Classification/001 Business Problem & Dataset_en.vtt 8KB
  131. 18 - Appendix/001 Text File Processing - I_en.vtt 8KB
  132. 07 - Project 4 _ Automated Text Summarization/001 Importing the libraries and Dataset_en.vtt 8KB
  133. 16 - Data analysis with Pandas/008 Merging, Joining and Concatenating DataFrames_en.vtt 8KB
  134. 08 - Project 5 _ Twitter sentiment Analysis/001 Setting up Twitter Developer application_en.vtt 8KB
  135. 07 - Project 4 _ Automated Text Summarization/002 Create Word Frequency Counter_en.vtt 7KB
  136. 01 - Welcome/003 Introduction to NLP_en.vtt 7KB
  137. 16 - Data analysis with Pandas/009 Pandas Operations_en.vtt 7KB
  138. 16 - Data analysis with Pandas/010 Reading and Writing Files in Pandas_en.vtt 7KB
  139. 16 - Data analysis with Pandas/007 Groupby Method_en.vtt 7KB
  140. 10 - Word Embeddings/004 Embeddings with Pretrained model_en.vtt 7KB
  141. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/003 Cleaning Text Data with NLTK - 2_en.vtt 7KB
  142. 15 - Data analysis with Numpy/005 Numpy Indexing and Selection Part 1_en.vtt 7KB
  143. 04 - Project 1 _ Spam Message Classification/003 Split Data in Training & Testing_en.vtt 7KB
  144. 12 - Project 7 _ Text Classification with RNN/004 Spam Detection with RNN_en.vtt 7KB
  145. 17 - Data Visualization with Matplotlib/004 Matplotlib Part 2 - Figure size, Aspect ratio and DPI_en.vtt 7KB
  146. 03 - Basics of Natural Language Processing/007 Stop Words_en.vtt 7KB
  147. 03 - Basics of Natural Language Processing/005 Stemming & Lemmatization - 1_en.vtt 7KB
  148. 07 - Project 4 _ Automated Text Summarization/003 Calculate Sentence Score_en.vtt 7KB
  149. 10 - Word Embeddings/003 Train Model for Embedding - II_en.vtt 7KB
  150. 13 - Project 8 _ Automatic Text Generation using TensorFlow, Keras and LSTM/002 Text Generation Part II_en.vtt 6KB
  151. 16 - Data analysis with Pandas/006 Missing Data_en.vtt 6KB
  152. 09 - Deep Learning Basics/001 The Neuron_en.vtt 6KB
  153. 17 - Data Visualization with Matplotlib/002 Matplotlib Part 1 - Object Oriented Method_en.vtt 6KB
  154. 14 - FastText Library for Text Classification/003 Virtual Box Installation_en.vtt 6KB
  155. 14 - FastText Library for Text Classification/005 Install fasttext library_en.vtt 6KB
  156. 03 - Basics of Natural Language Processing/006 Stemming & Lemmatization - 2_en.vtt 5KB
  157. 17 - Data Visualization with Matplotlib/003 Matplotlib Part 2 - Subplots Method_en.vtt 5KB
  158. 11 - Project 6 _ Text Classification with CNN/001 Convolutional Neural Network Part 1_en.vtt 5KB
  159. 04 - Project 1 _ Spam Message Classification/005 Apply Support vector Machine (SVM)_en.vtt 5KB
  160. 17 - Data Visualization with Matplotlib/005 Matplotlib Part 3_en.vtt 5KB
  161. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/005 Apply Naive Bayes Algorithm_en.vtt 5KB
  162. 05 - Project 2 _ Restaurant Review Prediction (Good or bad)/001 Business Problem_en.vtt 5KB
  163. 11 - Project 6 _ Text Classification with CNN/002 Convolutional Neural Network Part 2_en.vtt 4KB
  164. 15 - Data analysis with Numpy/004 Numpy Arrays Part 3_en.vtt 4KB
  165. 15 - Data analysis with Numpy/006 Numpy Indexing and Selection Part 2_en.vtt 4KB
  166. 01 - Welcome/001 Course Overview_en.vtt 4KB
  167. 03 - Basics of Natural Language Processing/010 Vocabulary and Matching Part - 3 (Phrase Based)_en.vtt 4KB
  168. 08 - Project 5 _ Twitter sentiment Analysis/002 Fetch Tweet from Tweeter server_en.vtt 4KB
  169. 09 - Deep Learning Basics/004 Gradient Descent and Back-Propagation_en.vtt 4KB
  170. 07 - Project 4 _ Automated Text Summarization/004 Extract summary of document_en.vtt 4KB
  171. 04 - Project 1 _ Spam Message Classification/006 Predict Testing Data both model_en.vtt 4KB
  172. 12 - Project 7 _ Text Classification with RNN/003 LSTM and GRU_en.vtt 4KB
  173. 18 - Appendix/004 Text File Processing - IV_en.vtt 3KB
  174. 15 - Data analysis with Numpy/007 Numpy Operations_en.vtt 3KB
  175. 02 - Installation & Setup/004 Links to Notebooks (More explanatory notebook for refrence).html 3KB
  176. 02 - Installation & Setup/003 Links to Notebooks (As taught in Lectures).html 3KB
  177. 15 - Data analysis with Numpy/002 Numpy Arrays Part 1_en.vtt 3KB
  178. 03 - Basics of Natural Language Processing/004 Tokenization Basic Part - 3_en.vtt 3KB
  179. 18 - Appendix/25154140-sample.pdf 3KB
  180. 03 - Basics of Natural Language Processing/001 Section _ Introduction_en.vtt 3KB
  181. 09 - Deep Learning Basics/003 Cost Function_en.vtt 3KB
  182. 14 - FastText Library for Text Classification/001 fasttext Installation steps [Video]_en.vtt 2KB
  183. 12 - Project 7 _ Text Classification with RNN/002 Vanishing Gradient Problem_en.vtt 2KB
  184. 12 - Project 7 _ Text Classification with RNN/001 Introduction to Recurrent Neural Networks_en.vtt 2KB
  185. 01 - Welcome/002 Reviews UPDATE_en.vtt 2KB
  186. 01 - Welcome/004 Course FAQs.html 2KB
  187. 15 - Data analysis with Numpy/001 Introduction to NumPy_en.vtt 949B
  188. 02 - Installation & Setup/002 Local Installation Steps.html 860B
  189. 16 - Data analysis with Pandas/001 Pandas Introduction_en.vtt 707B
  190. 14 - FastText Library for Text Classification/002 fasttext Installation steps [Text].html 466B
  191. 03 - Basics of Natural Language Processing/external-assets-links.txt 226B
  192. 04 - Project 1 _ Spam Message Classification/external-assets-links.txt 134B
  193. 02 - Installation & Setup/external-assets-links.txt 99B
  194. 02 - Installation & Setup/24056952-requirements.txt 12B