Udemy - Applied Text Mining and Sentiment Analysis with Python

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Udemy - Applied Text Mining and Sentiment Analysis with Python
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tutsnode
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Udemy - Applied Text Mining and Sentiment Analysis with Python
Language
English
Category
Other
Size
961.0 MB
Added
2023-06-01 23:46 (3 years, 5 months ago)
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Files
  1. 1. Preview.mp470.0 MB
  2. TutsNode.com.txt63 bytes
  3. [TGx]Downloaded from torrentgalaxy.to .txt585 bytes
  4. 4. (Python Practice) Cleaning Twitter Features.srt8.0 KB
  5. 3. Logistic Regression.srt7.7 KB
  6. 1. Section Overview.srt2.0 KB
  7. 7. Model Performance Measures.srt7.1 KB
  8. 6. (Python Practice) Cleaning General Features.srt6.6 KB
  9. 6. (Python Practice) ML Model Fitting.srt6.0 KB
  10. 0606 bytes
  11. 4. (Python Practice) Cleaning Twitter Features.mp438.0 MB
  12. 8.1 Colab_Notebook_Section_4_completed.ipynb85.3 KB
  13. 4. Text Mining and NLP.srt2.4 KB
  14. 8.1 Colab_Notebook_Section_3_completed.ipynb83.7 KB
  15. 5. Sentiment Analysis.srt2.7 KB
  16. 15.1 Colab_Notebook_Section_2_completed.ipynb82.0 KB
  17. 6. Roadmap.srt2.7 KB
  18. 10.1 Colab_Notebook_Section_1_completed.ipynb78.5 KB
  19. 7.1 Colab_Notebook.ipynb77.5 KB
  20. 6. (Python Practice) Applied Bag-of-Words.srt5.8 KB
  21. 4. ML Model Training.srt5.7 KB
  22. 7. Tokenization.srt5.3 KB
  23. 1. Preview.srt5.2 KB
  24. 7. TF-IDF.srt4.7 KB
  25. 9. (Python Practice) Dataset Overview.srt3.0 KB
  26. 3. PositiveNegative Word Frequencies.srt4.6 KB
  27. 3. Text Cleaning (12) - Twitter Features.srt4.2 KB
  28. 8. (Python Practice) Applied Performance Measures.srt4.0 KB
  29. 14. (Python Practice) Applied Lemmatization.srt3.9 KB
  30. 1. Section Overview.srt1.2 KB
  31. 1. Section Overview.srt1.4 KB
  32. 1166 bytes
  33. 3. Logistic Regression.mp437.4 MB
  34. 8. (Python Practice) Dataset Connection.srt3.8 KB
  35. 2. What is Text Normalization.srt3.7 KB
  36. 10. (Python Practice) Dataset Visualization.srt3.7 KB
  37. 4. (Python Practice) Applied PositiveNegative Frequencies.srt3.5 KB
  38. 5. Text Cleaning (22) - General Features.srt3.5 KB
  39. 2. What is Text.srt3.5 KB
  40. 5. Bag-of-Words.srt3.5 KB
  41. 10. (Python Practice) Applied Tokenization (33).srt3.4 KB
  42. 8. (Python Practice) Applied TF-IDF.srt3.4 KB
  43. 12. (Python Practice) Applied Stemming.srt3.3 KB
  44. 7. (Python Practice) Google Colab.srt3.2 KB
  45. 8. (Python Practice) Applied Tokenization (13).srt2.3 KB
  46. 11. Stemming.srt3.1 KB
  47. 9. (Python Practice) Applied Tokenization (23).srt2.4 KB
  48. 3. What is Text Mining.srt3.1 KB
  49. 5. (Python Practice) TrainTest split.srt2.8 KB
  50. 15. (Python Pratice) Tweet Pre-Processing.srt1.1 KB
  51. 298 bytes
  52. 4. ML Model Training.mp433.8 MB
  53. 2. Why Representing Text.srt2.6 KB
  54. 13. Lemmatization.srt2.5 KB
  55. 9. (Python Practice) Prediction Pipeline.srt2.1 KB
  56. 2. Why a model.srt1.7 KB
  57. 1. Section Overview.srt1.1 KB
  58. 3157.9 KB
  59. 7. Model Performance Measures.mp433.5 MB
  60. 434.4 KB
  61. 6. (Python Practice) Cleaning General Features.mp430.8 MB
  62. 5189.2 KB
  63. 6. (Python Practice) ML Model Fitting.mp429.5 MB
  64. 614.4 KB
  65. 6. (Python Practice) Applied Bag-of-Words.mp429.1 MB
  66. 7434.7 KB
  67. 1. Section Overview.mp429.0 MB
  68. 8466.0 KB
  69. 7. Tokenization.mp426.2 MB
  70. 9320.5 KB
  71. 7. TF-IDF.mp423.5 MB
  72. 1047.1 KB
  73. 3. PositiveNegative Word Frequencies.mp423.3 MB
  74. 11247.8 KB
  75. 1. Section Overview.mp422.5 MB
  76. 12486.8 KB
  77. 10. (Python Practice) Dataset Visualization.mp422.2 MB
  78. 13324.3 KB
  79. 3. Text Cleaning (12) - Twitter Features.mp422.2 MB
  80. 14327.3 KB
  81. 8. (Python Practice) Dataset Connection.mp421.2 MB
  82. 15263.6 KB
  83. 4. (Python Practice) Applied PositiveNegative Frequencies.mp421.0 MB
  84. 1638.8 KB
  85. 2. What is Text.mp420.5 MB
  86. 1724.0 KB
  87. 5. Bag-of-Words.mp419.6 MB
  88. 18409.8 KB
  89. 2. What is Text Normalization.mp419.6 MB
  90. 19459.9 KB
  91. 8. (Python Practice) Applied Performance Measures.mp419.1 MB
  92. 20397.1 KB
  93. 3. What is Text Mining.mp419.0 MB
  94. 21471.0 KB
  95. 12. (Python Practice) Applied Stemming.mp418.8 MB
  96. 22221.8 KB
  97. 5. Text Cleaning (22) - General Features.mp418.7 MB
  98. 23275.0 KB
  99. 14. (Python Practice) Applied Lemmatization.mp418.6 MB
  100. 24361.1 KB
  101. 1. Section Overview.mp418.6 MB
  102. 25442.6 KB
  103. 10. (Python Practice) Applied Tokenization (33).mp418.3 MB
  104. 26207.3 KB
  105. 11. Stemming.mp418.1 MB
  106. 27432.6 KB
  107. 8. (Python Practice) Applied TF-IDF.mp417.7 MB
  108. 28328.3 KB
  109. 2. Why Representing Text.mp417.6 MB
  110. 29398.4 KB
  111. 1. Section Overview.mp417.2 MB
  112. 30306.6 KB
  113. 5. (Python Practice) TrainTest split.mp416.9 MB
  114. 31109.1 KB
  115. 5. Sentiment Analysis.mp416.3 MB
  116. 32216.9 KB
  117. 9. (Python Practice) Dataset Overview.mp416.2 MB
  118. 33294.8 KB
  119. 6. Roadmap.mp416.2 MB
  120. 34321.9 KB
  121. 13. Lemmatization.mp414.8 MB
  122. 35232.7 KB
  123. 4. Text Mining and NLP.mp414.6 MB
  124. 36399.6 KB
  125. 9. (Python Practice) Prediction Pipeline.mp412.6 MB
  126. 37379.9 KB
  127. 8. (Python Practice) Applied Tokenization (13).mp412.6 MB
  128. 38417.7 KB
  129. 7. (Python Practice) Google Colab.mp412.3 MB
  130. 39157.7 KB
  131. 9. (Python Practice) Applied Tokenization (23).mp411.9 MB
  132. 4080.3 KB
  133. 2. Why a model.mp411.7 MB
  134. 41320.5 KB
  135. 15. (Python Pratice) Tweet Pre-Processing.mp48.4 MB
  136. 42132.2 KB
  137. 2.1 Section 1 - Theory Deck.pdf2.6 MB
  138. 43425.9 KB
  139. 2.1 Section 2 - Theory Deck.pdf1.8 MB
  140. 44202.0 KB
  141. 8.1 tweet_data.csv1.8 MB
  142. 45255.2 KB
  143. 2.1 Section 4 - Theory Deck.pdf1.6 MB
  144. 46436.8 KB
  145. 2.1 Section 3 - Theory Deck.pdf1.5 MB

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