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

  • 收录时间:2020-02-05 04:10:13
  • 文件大小:8GB
  • 下载次数:86
  • 最近下载:2021-01-18 15:23:53
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文件列表

  1. 6. NLP Core/25. LSA in Python Part 1.mp4 296MB
  2. 5. Numpy and Pandas/1. Introduction to Numpy.mp4 281MB
  3. 6. NLP Core/21. Understanding the N-Gram Model.mp4 259MB
  4. 5. Numpy and Pandas/2. Introduction to Pandas.mp4 252MB
  5. 6. NLP Core/16. Text Modelling using TF-IDF Model.mp4 223MB
  6. 7. Project 1 - Text Classification/9. Understanding Logistic Regression.mp4 202MB
  7. 6. NLP Core/24. Understanding Latent Semantic Analysis.mp4 194MB
  8. 6. NLP Core/26. LSA in Python Part 2.mp4 190MB
  9. 6. NLP Core/22. Building Character N-Gram Model.mp4 186MB
  10. 4. Regular Expressions/5. Shorthand Character Classes.mp4 182MB
  11. 3. Python Crash Course/11. List Comprehension.mp4 165MB
  12. 10. Word2Vec Analysis/1. Understanding Word Vectors.mp4 161MB
  13. 6. NLP Core/23. Building Word N-Gram Model.mp4 161MB
  14. 6. NLP Core/11. Text Modelling using Bag of Words Model.mp4 146MB
  15. 6. NLP Core/7. Stop word removal using NLTK.mp4 140MB
  16. 6. NLP Core/5. Stemming using NLTK.mp4 134MB
  17. 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.mp4 133MB
  18. 3. Python Crash Course/5. Python Data Structures - Lists.mp4 129MB
  19. 3. Python Crash Course/7. Python Data Structures - Dictionaries.mp4 125MB
  20. 6. NLP Core/18. Building the TF-IDF Model Part 2.mp4 123MB
  21. 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.mp4 118MB
  22. 7. Project 1 - Text Classification/6. Transforming data into BOW Model.mp4 115MB
  23. 6. NLP Core/17. Building the TF-IDF Model Part 1.mp4 110MB
  24. 6. NLP Core/19. Building the TF-IDF Model Part 3.mp4 110MB
  25. 6. NLP Core/8. Parts Of Speech Tagging.mp4 109MB
  26. 10. Word2Vec Analysis/6. Improving the Model.mp4 108MB
  27. 6. NLP Core/15. Building the BOW Model Part 4.mp4 108MB
  28. 6. NLP Core/4. Introduction to Stemming and Lemmatization.mp4 108MB
  29. 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.mp4 103MB
  30. 9. Project 3 - Text Summarization/7. Calculating the sentence scores.mp4 100MB
  31. 3. Python Crash Course/8. Console and File IO in Python.mp4 97MB
  32. 7. Project 1 - Text Classification/12. Saving our Model.mp4 97MB
  33. 9. Project 3 - Text Summarization/1. Understanding Text Summarization.mp4 96MB
  34. 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.mp4 94MB
  35. 3. Python Crash Course/10. Introduction to Classes and Objects.mp4 92MB
  36. 6. NLP Core/28. Word Negation Tracking in Python Part 1.mp4 91MB
  37. 6. NLP Core/12. Building the BOW Model Part 1.mp4 89MB
  38. 7. Project 1 - Text Classification/11. Testing Model performance.mp4 84MB
  39. 6. NLP Core/13. Building the BOW Model Part 2.mp4 82MB
  40. 4. Regular Expressions/3. Finding Patterns in Text Part 2.mp4 81MB
  41. 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.mp4 81MB
  42. 4. Regular Expressions/2. Finding Patterns in Text Part 1.mp4 79MB
  43. 6. NLP Core/14. Building the BOW Model Part 3.mp4 77MB
  44. 9. Project 3 - Text Summarization/8. Getting the summary.mp4 77MB
  45. 3. Python Crash Course/9. Introduction to Functions.mp4 77MB
  46. 6. NLP Core/6. Lemmatization using NLTK.mp4 76MB
  47. 1. Introduction to the Course/1. What is NLP.mp4 76MB
  48. 6. NLP Core/2. Tokenizing Words and Sentences.mp4 75MB
  49. 7. Project 1 - Text Classification/8. Creating training and test set.mp4 72MB
  50. 4. Regular Expressions/7. Preprocessing using Regex.mp4 72MB
  51. 7. Project 1 - Text Classification/4. Persisting the dataset.mp4 72MB
  52. 7. Project 1 - Text Classification/5. Preprocessing the data.mp4 67MB
  53. 3. Python Crash Course/3. Introduction to Loops.mp4 65MB
  54. 6. NLP Core/20. Building the TF-IDF Model Part 4.mp4 65MB
  55. 3. Python Crash Course/2. Conditional Statements.mp4 64MB
  56. 4. Regular Expressions/1. Introduction to Regular Expressions.mp4 63MB
  57. 7. Project 1 - Text Classification/1. Getting the data for Text Classification.mp4 62MB
  58. 3. Python Crash Course/4. Loop Control Statements.mp4 62MB
  59. 3. Python Crash Course/6. Python Data Structures - Tuples.mp4 61MB
  60. 3. Python Crash Course/1. Variables and Operations in Python.mp4 60MB
  61. 6. NLP Core/29. Word Negation Tracking in Python Part 2.mp4 59MB
  62. 9. Project 3 - Text Summarization/6. Building the histogram.mp4 59MB
  63. 7. Project 1 - Text Classification/3. Importing the dataset.mp4 58MB
  64. 7. Project 1 - Text Classification/13. Importing and using our Model.mp4 56MB
  65. 6. NLP Core/10. Named Entity Recognition.mp4 56MB
  66. 10. Word2Vec Analysis/2. Importing the data.mp4 55MB
  67. 10. Word2Vec Analysis/5. Testing Model Performance.mp4 54MB
  68. 4. Regular Expressions/4. Substituting Patterns in Text.mp4 54MB
  69. 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.mp4 51MB
  70. 10. Word2Vec Analysis/7. Exploring Pre-trained Models.mp4 50MB
  71. 9. Project 3 - Text Summarization/4. Preprocessing the data.mp4 48MB
  72. 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.mp4 47MB
  73. 2. Getting the required softwares/3. A tour of Spyder IDE.mp4 47MB
  74. 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.mp4 47MB
  75. 9. Project 3 - Text Summarization/2. Fetching article data from the web.mp4 44MB
  76. 10. Word2Vec Analysis/3. Preparing the data.mp4 39MB
  77. 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.mp4 38MB
  78. 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.mp4 36MB
  79. 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.mp4 35MB
  80. 10. Word2Vec Analysis/4. Training the Word2Vec Model.mp4 34MB
  81. 2. Getting the required softwares/1. Installing Anaconda Python.mp4 33MB
  82. 7. Project 1 - Text Classification/10. Training our classifier.mp4 31MB
  83. 6. NLP Core/1. Installing NLTK in Python.mp4 29MB
  84. 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.mp4 28MB
  85. 1. Introduction to the Course/2. Getting the Course Resources.mp4 18MB
  86. 5. Numpy and Pandas/2. Introduction to Pandas.srt 29KB
  87. 5. Numpy and Pandas/1. Introduction to Numpy.srt 27KB
  88. 6. NLP Core/21. Understanding the N-Gram Model.srt 27KB
  89. 6. NLP Core/25. LSA in Python Part 1.srt 26KB
  90. 5. Numpy and Pandas/2. Introduction to Pandas.vtt 25KB
  91. 6. NLP Core/21. Understanding the N-Gram Model.vtt 23KB
  92. 5. Numpy and Pandas/1. Introduction to Numpy.vtt 23KB
  93. 6. NLP Core/25. LSA in Python Part 1.vtt 22KB
  94. 6. NLP Core/16. Text Modelling using TF-IDF Model.srt 22KB
  95. 7. Project 1 - Text Classification/9. Understanding Logistic Regression.srt 20KB
  96. 6. NLP Core/22. Building Character N-Gram Model.srt 20KB
  97. 6. NLP Core/24. Understanding Latent Semantic Analysis.srt 19KB
  98. 6. NLP Core/16. Text Modelling using TF-IDF Model.vtt 19KB
  99. 7. Project 1 - Text Classification/9. Understanding Logistic Regression.vtt 18KB
  100. 6. NLP Core/22. Building Character N-Gram Model.vtt 18KB
  101. 4. Regular Expressions/5. Shorthand Character Classes.srt 17KB
  102. 6. NLP Core/24. Understanding Latent Semantic Analysis.vtt 17KB
  103. 3. Python Crash Course/11. List Comprehension.srt 17KB
  104. 3. Python Crash Course/5. Python Data Structures - Lists.srt 16KB
  105. 10. Word2Vec Analysis/1. Understanding Word Vectors.srt 16KB
  106. 4. Regular Expressions/5. Shorthand Character Classes.vtt 15KB
  107. 6. NLP Core/26. LSA in Python Part 2.srt 15KB
  108. 6. NLP Core/23. Building Word N-Gram Model.srt 15KB
  109. 6. NLP Core/11. Text Modelling using Bag of Words Model.srt 15KB
  110. 3. Python Crash Course/11. List Comprehension.vtt 14KB
  111. 3. Python Crash Course/7. Python Data Structures - Dictionaries.srt 14KB
  112. 10. Word2Vec Analysis/1. Understanding Word Vectors.vtt 14KB
  113. 3. Python Crash Course/5. Python Data Structures - Lists.vtt 14KB
  114. 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.srt 13KB
  115. 6. NLP Core/26. LSA in Python Part 2.vtt 13KB
  116. 6. NLP Core/23. Building Word N-Gram Model.vtt 13KB
  117. 6. NLP Core/11. Text Modelling using Bag of Words Model.vtt 13KB
  118. 6. NLP Core/28. Word Negation Tracking in Python Part 1.srt 13KB
  119. 3. Python Crash Course/7. Python Data Structures - Dictionaries.vtt 12KB
  120. 6. NLP Core/27. Word Synonyms and Antonyms using NLTK.vtt 11KB
  121. 6. NLP Core/28. Word Negation Tracking in Python Part 1.vtt 11KB
  122. 4. Regular Expressions/2. Finding Patterns in Text Part 1.srt 11KB
  123. 6. NLP Core/4. Introduction to Stemming and Lemmatization.srt 10KB
  124. 4. Regular Expressions/3. Finding Patterns in Text Part 2.srt 10KB
  125. 3. Python Crash Course/3. Introduction to Loops.srt 10KB
  126. 9. Project 3 - Text Summarization/1. Understanding Text Summarization.srt 10KB
  127. 7. Project 1 - Text Classification/6. Transforming data into BOW Model.srt 10KB
  128. 3. Python Crash Course/8. Console and File IO in Python.srt 10KB
  129. 3. Python Crash Course/1. Variables and Operations in Python.srt 9KB
  130. 4. Regular Expressions/2. Finding Patterns in Text Part 1.vtt 9KB
  131. 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.srt 9KB
  132. 6. NLP Core/18. Building the TF-IDF Model Part 2.srt 9KB
  133. 3. Python Crash Course/10. Introduction to Classes and Objects.srt 9KB
  134. 3. Python Crash Course/4. Loop Control Statements.srt 9KB
  135. 6. NLP Core/4. Introduction to Stemming and Lemmatization.vtt 9KB
  136. 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.srt 9KB
  137. 4. Regular Expressions/3. Finding Patterns in Text Part 2.vtt 9KB
  138. 6. NLP Core/7. Stop word removal using NLTK.srt 9KB
  139. 3. Python Crash Course/3. Introduction to Loops.vtt 9KB
  140. 7. Project 1 - Text Classification/6. Transforming data into BOW Model.vtt 9KB
  141. 9. Project 3 - Text Summarization/1. Understanding Text Summarization.vtt 9KB
  142. 6. NLP Core/5. Stemming using NLTK.srt 8KB
  143. 6. NLP Core/15. Building the BOW Model Part 4.srt 8KB
  144. 3. Python Crash Course/8. Console and File IO in Python.vtt 8KB
  145. 6. NLP Core/19. Building the TF-IDF Model Part 3.srt 8KB
  146. 3. Python Crash Course/9. Introduction to Functions.srt 8KB
  147. 3. Python Crash Course/1. Variables and Operations in Python.vtt 8KB
  148. 6. NLP Core/18. Building the TF-IDF Model Part 2.vtt 8KB
  149. 3. Python Crash Course/10. Introduction to Classes and Objects.vtt 8KB
  150. 6. NLP Core/17. Building the TF-IDF Model Part 1.srt 8KB
  151. 9. Project 3 - Text Summarization/3. Parsing the data using Beautiful Soup.vtt 8KB
  152. 3. Python Crash Course/4. Loop Control Statements.vtt 8KB
  153. 6. NLP Core/29. Word Negation Tracking in Python Part 2.srt 8KB
  154. 4. Regular Expressions/4. Substituting Patterns in Text.srt 8KB
  155. 4. Regular Expressions/7. Preprocessing using Regex.srt 8KB
  156. 9. Project 3 - Text Summarization/7. Calculating the sentence scores.srt 8KB
  157. 10. Word2Vec Analysis/6. Improving the Model.srt 8KB
  158. 6. NLP Core/8. Parts Of Speech Tagging.srt 8KB
  159. 7. Project 1 - Text Classification/12. Saving our Model.srt 8KB
  160. 1. Introduction to the Course/1. What is NLP.srt 8KB
  161. 7. Project 1 - Text Classification/1. Getting the data for Text Classification.srt 8KB
  162. 8. Project 2 - Twitter Sentiment Analysis/8. Plotting the results.vtt 8KB
  163. 6. NLP Core/7. Stop word removal using NLTK.vtt 8KB
  164. 6. NLP Core/5. Stemming using NLTK.vtt 7KB
  165. 6. NLP Core/15. Building the BOW Model Part 4.vtt 7KB
  166. 6. NLP Core/19. Building the TF-IDF Model Part 3.vtt 7KB
  167. 3. Python Crash Course/9. Introduction to Functions.vtt 7KB
  168. 6. NLP Core/17. Building the TF-IDF Model Part 1.vtt 7KB
  169. 7. Project 1 - Text Classification/11. Testing Model performance.srt 7KB
  170. 3. Python Crash Course/6. Python Data Structures - Tuples.srt 7KB
  171. 6. NLP Core/29. Word Negation Tracking in Python Part 2.vtt 7KB
  172. 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.srt 7KB
  173. 4. Regular Expressions/4. Substituting Patterns in Text.vtt 7KB
  174. 3. Python Crash Course/2. Conditional Statements.srt 7KB
  175. 9. Project 3 - Text Summarization/7. Calculating the sentence scores.vtt 7KB
  176. 4. Regular Expressions/7. Preprocessing using Regex.vtt 7KB
  177. 6. NLP Core/10. Named Entity Recognition.srt 7KB
  178. 7. Project 1 - Text Classification/12. Saving our Model.vtt 7KB
  179. 6. NLP Core/8. Parts Of Speech Tagging.vtt 7KB
  180. 10. Word2Vec Analysis/7. Exploring Pre-trained Models.srt 7KB
  181. 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.srt 7KB
  182. 10. Word2Vec Analysis/6. Improving the Model.vtt 7KB
  183. 7. Project 1 - Text Classification/1. Getting the data for Text Classification.vtt 7KB
  184. 1. Introduction to the Course/1. What is NLP.vtt 7KB
  185. 7. Project 1 - Text Classification/3. Importing the dataset.srt 7KB
  186. 10. Word2Vec Analysis/2. Importing the data.srt 6KB
  187. 7. Project 1 - Text Classification/4. Persisting the dataset.srt 6KB
  188. 7. Project 1 - Text Classification/11. Testing Model performance.vtt 6KB
  189. 4. Regular Expressions/1. Introduction to Regular Expressions.srt 6KB
  190. 3. Python Crash Course/6. Python Data Structures - Tuples.vtt 6KB
  191. 2. Getting the required softwares/3. A tour of Spyder IDE.srt 6KB
  192. 3. Python Crash Course/2. Conditional Statements.vtt 6KB
  193. 8. Project 2 - Twitter Sentiment Analysis/6. Preprocessing the tweets.vtt 6KB
  194. 7. Project 1 - Text Classification/5. Preprocessing the data.srt 6KB
  195. 6. NLP Core/13. Building the BOW Model Part 2.srt 6KB
  196. 6. NLP Core/10. Named Entity Recognition.vtt 6KB
  197. 9. Project 3 - Text Summarization/8. Getting the summary.srt 6KB
  198. 10. Word2Vec Analysis/7. Exploring Pre-trained Models.vtt 6KB
  199. 9. Project 3 - Text Summarization/2. Fetching article data from the web.srt 6KB
  200. 8. Project 2 - Twitter Sentiment Analysis/4. Fetching real time tweets.vtt 6KB
  201. 7. Project 1 - Text Classification/3. Importing the dataset.vtt 6KB
  202. 6. NLP Core/14. Building the BOW Model Part 3.srt 6KB
  203. 7. Project 1 - Text Classification/8. Creating training and test set.srt 6KB
  204. 7. Project 1 - Text Classification/4. Persisting the dataset.vtt 6KB
  205. 10. Word2Vec Analysis/2. Importing the data.vtt 6KB
  206. 6. NLP Core/12. Building the BOW Model Part 1.srt 5KB
  207. 9. Project 3 - Text Summarization/6. Building the histogram.srt 5KB
  208. 4. Regular Expressions/1. Introduction to Regular Expressions.vtt 5KB
  209. 6. NLP Core/2. Tokenizing Words and Sentences.srt 5KB
  210. 6. NLP Core/1. Installing NLTK in Python.srt 5KB
  211. 2. Getting the required softwares/3. A tour of Spyder IDE.vtt 5KB
  212. 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.srt 5KB
  213. 6. NLP Core/20. Building the TF-IDF Model Part 4.srt 5KB
  214. 6. NLP Core/13. Building the BOW Model Part 2.vtt 5KB
  215. 7. Project 1 - Text Classification/5. Preprocessing the data.vtt 5KB
  216. 9. Project 3 - Text Summarization/8. Getting the summary.vtt 5KB
  217. 9. Project 3 - Text Summarization/2. Fetching article data from the web.vtt 5KB
  218. 7. Project 1 - Text Classification/8. Creating training and test set.vtt 5KB
  219. 7. Project 1 - Text Classification/13. Importing and using our Model.srt 5KB
  220. 6. NLP Core/14. Building the BOW Model Part 3.vtt 5KB
  221. 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.srt 5KB
  222. 10. Word2Vec Analysis/5. Testing Model Performance.srt 5KB
  223. 6. NLP Core/12. Building the BOW Model Part 1.vtt 5KB
  224. 9. Project 3 - Text Summarization/6. Building the histogram.vtt 5KB
  225. 6. NLP Core/1. Installing NLTK in Python.vtt 5KB
  226. 6. NLP Core/2. Tokenizing Words and Sentences.vtt 5KB
  227. 8. Project 2 - Twitter Sentiment Analysis/2. Initializing Tokens.vtt 5KB
  228. 6. NLP Core/20. Building the TF-IDF Model Part 4.vtt 5KB
  229. 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.srt 5KB
  230. 6. NLP Core/6. Lemmatization using NLTK.srt 4KB
  231. 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.srt 4KB
  232. 2. Getting the required softwares/1. Installing Anaconda Python.srt 4KB
  233. 8. Project 2 - Twitter Sentiment Analysis/1. Setting up Twitter Application.vtt 4KB
  234. 7. Project 1 - Text Classification/13. Importing and using our Model.vtt 4KB
  235. 10. Word2Vec Analysis/5. Testing Model Performance.vtt 4KB
  236. 10. Word2Vec Analysis/3. Preparing the data.srt 4KB
  237. 9. Project 3 - Text Summarization/4. Preprocessing the data.srt 4KB
  238. 8. Project 2 - Twitter Sentiment Analysis/3. Client Authentication.vtt 4KB
  239. 2. Getting the required softwares/1. Installing Anaconda Python.vtt 4KB
  240. 9. Project 3 - Text Summarization/5. Tokenizing Article into sentences.vtt 4KB
  241. 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.srt 4KB
  242. 6. NLP Core/6. Lemmatization using NLTK.vtt 4KB
  243. 10. Word2Vec Analysis/3. Preparing the data.vtt 4KB
  244. 9. Project 3 - Text Summarization/4. Preprocessing the data.vtt 4KB
  245. 10. Word2Vec Analysis/4. Training the Word2Vec Model.srt 3KB
  246. 7. Project 1 - Text Classification/7. Transform BOW model into TF-IDF Model.vtt 3KB
  247. 6. NLP Core/9. POS Tag Meanings.html 3KB
  248. 10. Word2Vec Analysis/4. Training the Word2Vec Model.vtt 3KB
  249. 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.srt 3KB
  250. 7. Project 1 - Text Classification/10. Training our classifier.srt 2KB
  251. 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.srt 2KB
  252. 8. Project 2 - Twitter Sentiment Analysis/5. Loading TF-IDF Model and Classifier.vtt 2KB
  253. 1. Introduction to the Course/2. Getting the Course Resources.srt 2KB
  254. 7. Project 1 - Text Classification/10. Training our classifier.vtt 2KB
  255. 8. Project 2 - Twitter Sentiment Analysis/7. Predicting sentiments of tweets.vtt 2KB
  256. 1. Introduction to the Course/2. Getting the Course Resources.vtt 2KB
  257. 2. Getting the required softwares/4. How to take this course.html 2KB
  258. 6. NLP Core/3. How tokenization works - Text.html 2KB
  259. 4. Regular Expressions/6. Character Ranges - Text.html 1KB
  260. 7. Project 1 - Text Classification/2. Getting the data for Text Classification - Text.html 806B
  261. 2. Getting the required softwares/2. Installing Anaconda Python - Text.html 734B
  262. 11. Conclusion/1. Where you go from here.html 727B
  263. 1. Introduction to the Course/3. Getting the Course Resources - Text.html 614B
  264. 3. Python Crash Course/12. Test Your Skills.html 156B
  265. 4. Regular Expressions/8. Test Your Skills.html 156B
  266. [FCS Forum].url 133B
  267. [FreeCourseSite.com].url 127B
  268. [CourseClub.NET].url 123B