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[] Udemy - Machine Learning with Imbalanced Data

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文件列表

  1. 3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4 87MB
  2. 3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.mp4 80MB
  3. 8. Cost Sensitive Learning/9. Bayes Conditional Risk.mp4 72MB
  4. 7. Ensemble Methods/8. Ensemble Methods - Demo.mp4 71MB
  5. 7. Ensemble Methods/5. Boosting.mp4 71MB
  6. 3. Evaluation Metrics/4. Precision, Recall and F-measure.mp4 67MB
  7. 4. Udersampling/3. Random Under-Sampling - Demo.mp4 67MB
  8. 9. Probability Calibration/3. Probability Calibration Curves - Demo.mp4 65MB
  9. 8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.mp4 56MB
  10. 4. Udersampling/5. Condensed Nearest Neighbours - Demo.mp4 53MB
  11. 9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4 52MB
  12. 3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.mp4 49MB
  13. 9. Probability Calibration/5. Brier Score - Demo.mp4 49MB
  14. 5. Oversampling/6. SMOTE-NC.mp4 48MB
  15. 3. Evaluation Metrics/3. Accuracy - Demo.mp4 48MB
  16. 4. Udersampling/22. Undersampling Method Comparison.mp4 48MB
  17. 7. Ensemble Methods/6. Boosting plus Re-Sampling.mp4 47MB
  18. 9. Probability Calibration/8. Calibrating a Classifier - Demo.mp4 47MB
  19. 5. Oversampling/10. Borderline SMOTE.mp4 46MB
  20. 5. Oversampling/4. SMOTE.mp4 45MB
  21. 8. Cost Sensitive Learning/2. Types of Cost.mp4 44MB
  22. 7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.mp4 43MB
  23. 8. Cost Sensitive Learning/10. MetaCost.mp4 43MB
  24. 3. Evaluation Metrics/13. Precision-Recall Curve.mp4 41MB
  25. 5. Oversampling/16. Over-Sampling Method Comparison.mp4 40MB
  26. 3. Evaluation Metrics/11. ROC-AUC.mp4 39MB
  27. 5. Oversampling/13. SVM SMOTE - Demo.mp4 37MB
  28. 8. Cost Sensitive Learning/12. Optional MetaCost Base Code.mp4 37MB
  29. 6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.mp4 37MB
  30. 6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.mp4 37MB
  31. 5. Oversampling/3. Random Over-Sampling - Demo.mp4 35MB
  32. 2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.mp4 35MB
  33. 6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.mp4 34MB
  34. 9. Probability Calibration/1. Probability Calibration.mp4 34MB
  35. 2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.mp4 33MB
  36. 8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.mp4 33MB
  37. 4. Udersampling/4. Condensed Nearest Neighbours - Intro.mp4 32MB
  38. 1. Introduction/1. Introduction.mp4 32MB
  39. 5. Oversampling/8. ADASYN.mp4 32MB
  40. 3. Evaluation Metrics/12. ROC-AUC - Demo.mp4 32MB
  41. 4. Udersampling/1. Under-Sampling Methods - Introduction.mp4 31MB
  42. 4. Udersampling/11. Edited Nearest Neighbours - Demo.mp4 31MB
  43. 4. Udersampling/21. Instance Hardness Threshold - Demo.mp4 31MB
  44. 7. Ensemble Methods/7. Hybdrid Methods.mp4 30MB
  45. 3. Evaluation Metrics/7. Confusion tables, FPR and FNR.mp4 30MB
  46. 9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4 30MB
  47. 9. Probability Calibration/2. Probability Calibration Curves.mp4 29MB
  48. 5. Oversampling/14. K-Means SMOTE.mp4 28MB
  49. 9. Probability Calibration/7. Calibrating a Classifier.mp4 27MB
  50. 7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.mp4 27MB
  51. 4. Udersampling/19. NearMiss - Demo.mp4 26MB
  52. 4. Udersampling/2. Random Under-Sampling - Intro.mp4 26MB
  53. 4. Udersampling/9. One Sided Selection - Demo.mp4 26MB
  54. 5. Oversampling/12. SVM SMOTE.mp4 25MB
  55. 9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.mp4 25MB
  56. 5. Oversampling/15. K-Means SMOTE - Demo.mp4 25MB
  57. 5. Oversampling/11. Borderline SMOTE - Demo.mp4 25MB
  58. 4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.mp4 24MB
  59. 4. Udersampling/7. Tomek Links - Demo.mp4 24MB
  60. 3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4 23MB
  61. 4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.mp4 23MB
  62. 8. Cost Sensitive Learning/11. MetaCost - Demo.mp4 23MB
  63. 8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.mp4 23MB
  64. 4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.mp4 23MB
  65. 4. Udersampling/15. All KNN - Demo.mp4 23MB
  66. 4. Udersampling/10. Edited Nearest Neighbours - Intro.mp4 23MB
  67. 3. Evaluation Metrics/2. Accuracy.mp4 21MB
  68. 5. Oversampling/7. SMOTE-NC - Demo.mp4 21MB
  69. 8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.mp4 21MB
  70. 5. Oversampling/1. Over-Sampling Methods - Introduction.mp4 21MB
  71. 5. Oversampling/9. ADASYN - Demo.mp4 21MB
  72. 3. Evaluation Metrics/16. Probability.mp4 21MB
  73. 2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.mp4 20MB
  74. 7. Ensemble Methods/2. Foundations of Ensemble Learning.mp4 20MB
  75. 4. Udersampling/20. Instance Hardness Threshold - Intro.mp4 20MB
  76. 4. Udersampling/6. Tomek Links - Intro.mp4 19MB
  77. 8. Cost Sensitive Learning/3. Obtaining the Cost.mp4 19MB
  78. 8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.mp4 19MB
  79. 5. Oversampling/5. SMOTE - Demo.mp4 18MB
  80. 7. Ensemble Methods/3. Bagging.mp4 18MB
  81. 3. Evaluation Metrics/14. Precision-Recall Curve - Demo.mp4 18MB
  82. 1. Introduction/2. Course Curriculum Overview.mp4 18MB
  83. 4. Udersampling/18. NearMiss - Intro.mp4 17MB
  84. 9. Probability Calibration/4. Brier Score.mp4 17MB
  85. 4. Udersampling/14. All KNN - Intro.mp4 16MB
  86. 4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.mp4 16MB
  87. 5. Oversampling/2. Random Over-Sampling.mp4 16MB
  88. 4. Udersampling/8. One Sided Selection - Intro.mp4 12MB
  89. 1. Introduction/3. Course Material.mp4 11MB
  90. 3. Evaluation Metrics/1. Introduction to Performance Metrics.mp4 11MB
  91. 8. Cost Sensitive Learning/4. Cost Sensitive Approaches.mp4 10MB
  92. 4. Udersampling/23.1 Undersampling-Comparison.pdf 206KB
  93. 3. Evaluation Metrics/4. Precision, Recall and F-measure.srt 15KB
  94. 8. Cost Sensitive Learning/9. Bayes Conditional Risk.srt 15KB
  95. 4. Udersampling/3. Random Under-Sampling - Demo.srt 13KB
  96. 3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt 12KB
  97. 3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.srt 12KB
  98. 8. Cost Sensitive Learning/2. Types of Cost.srt 12KB
  99. 7. Ensemble Methods/8. Ensemble Methods - Demo.srt 12KB
  100. 9. Probability Calibration/3. Probability Calibration Curves - Demo.srt 12KB
  101. 7. Ensemble Methods/5. Boosting.srt 11KB
  102. 5. Oversampling/6. SMOTE-NC.srt 10KB
  103. 9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.srt 10KB
  104. 5. Oversampling/4. SMOTE.srt 10KB
  105. 3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.srt 10KB
  106. 5. Oversampling/10. Borderline SMOTE.srt 9KB
  107. 4. Udersampling/22. Undersampling Method Comparison.srt 9KB
  108. 3. Evaluation Metrics/13. Precision-Recall Curve.srt 9KB
  109. 4. Udersampling/5. Condensed Nearest Neighbours - Demo.srt 9KB
  110. 8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.srt 9KB
  111. 9. Probability Calibration/5. Brier Score - Demo.srt 9KB
  112. 8. Cost Sensitive Learning/10. MetaCost.srt 9KB
  113. 3. Evaluation Metrics/11. ROC-AUC.srt 8KB
  114. 4. Udersampling/4. Condensed Nearest Neighbours - Intro.srt 8KB
  115. 7. Ensemble Methods/6. Boosting plus Re-Sampling.srt 8KB
  116. 8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.srt 8KB
  117. 5. Oversampling/8. ADASYN.srt 8KB
  118. 8. Cost Sensitive Learning/12. Optional MetaCost Base Code.srt 7KB
  119. 3. Evaluation Metrics/7. Confusion tables, FPR and FNR.srt 7KB
  120. 9. Probability Calibration/1. Probability Calibration.srt 7KB
  121. 3. Evaluation Metrics/3. Accuracy - Demo.srt 7KB
  122. 9. Probability Calibration/8. Calibrating a Classifier - Demo.srt 7KB
  123. 6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.srt 7KB
  124. 5. Oversampling/16. Over-Sampling Method Comparison.srt 7KB
  125. 9. Probability Calibration/2. Probability Calibration Curves.srt 7KB
  126. 4. Udersampling/2. Random Under-Sampling - Intro.srt 7KB
  127. 4. Udersampling/1. Under-Sampling Methods - Introduction.srt 7KB
  128. 6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.srt 7KB
  129. 2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.srt 6KB
  130. 7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.srt 6KB
  131. 5. Oversampling/3. Random Over-Sampling - Demo.srt 6KB
  132. 6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.srt 6KB
  133. 9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt 6KB
  134. 5. Oversampling/12. SVM SMOTE.srt 6KB
  135. 5. Oversampling/14. K-Means SMOTE.srt 6KB
  136. 2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.srt 6KB
  137. 9. Probability Calibration/7. Calibrating a Classifier.srt 6KB
  138. 3. Evaluation Metrics/16. Probability.srt 6KB
  139. 4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.srt 5KB
  140. 7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.srt 5KB
  141. 4. Udersampling/10. Edited Nearest Neighbours - Intro.srt 5KB
  142. 3. Evaluation Metrics/12. ROC-AUC - Demo.srt 5KB
  143. 3. Evaluation Metrics/2. Accuracy.srt 5KB
  144. 7. Ensemble Methods/7. Hybdrid Methods.srt 5KB
  145. 4. Udersampling/6. Tomek Links - Intro.srt 5KB
  146. 3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt 5KB
  147. 4. Udersampling/11. Edited Nearest Neighbours - Demo.srt 5KB
  148. 4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.srt 5KB
  149. 4. Udersampling/20. Instance Hardness Threshold - Intro.srt 5KB
  150. 5. Oversampling/13. SVM SMOTE - Demo.srt 5KB
  151. 4. Udersampling/21. Instance Hardness Threshold - Demo.srt 5KB
  152. 2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.srt 5KB
  153. 4. Udersampling/9. One Sided Selection - Demo.srt 5KB
  154. 9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.srt 5KB
  155. 8. Cost Sensitive Learning/3. Obtaining the Cost.srt 5KB
  156. 4. Udersampling/19. NearMiss - Demo.srt 5KB
  157. 8. Cost Sensitive Learning/11. MetaCost - Demo.srt 4KB
  158. 4. Udersampling/18. NearMiss - Intro.srt 4KB
  159. 8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.srt 4KB
  160. 5. Oversampling/1. Over-Sampling Methods - Introduction.srt 4KB
  161. 4. Udersampling/14. All KNN - Intro.srt 4KB
  162. 8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.srt 4KB
  163. 4. Udersampling/7. Tomek Links - Demo.srt 4KB
  164. 1. Introduction/1. Introduction.srt 4KB
  165. 4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.srt 4KB
  166. 5. Oversampling/15. K-Means SMOTE - Demo.srt 4KB
  167. 1. Introduction/2. Course Curriculum Overview.srt 4KB
  168. 5. Oversampling/9. ADASYN - Demo.srt 4KB
  169. 5. Oversampling/2. Random Over-Sampling.srt 4KB
  170. 9. Probability Calibration/4. Brier Score.srt 4KB
  171. 8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.srt 4KB
  172. 5. Oversampling/11. Borderline SMOTE - Demo.srt 4KB
  173. 4. Udersampling/15. All KNN - Demo.srt 4KB
  174. 3. Evaluation Metrics/14. Precision-Recall Curve - Demo.srt 3KB
  175. 5. Oversampling/7. SMOTE-NC - Demo.srt 3KB
  176. 3. Evaluation Metrics/1. Introduction to Performance Metrics.srt 3KB
  177. 7. Ensemble Methods/3. Bagging.srt 3KB
  178. 7. Ensemble Methods/2. Foundations of Ensemble Learning.srt 3KB
  179. 5. Oversampling/5. SMOTE - Demo.srt 3KB
  180. 4. Udersampling/8. One Sided Selection - Intro.srt 3KB
  181. 4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.srt 3KB
  182. 1. Introduction/8. Additional resources for Machine Learning and Python programming.html 3KB
  183. 1. Introduction/3. Course Material.srt 2KB
  184. 7. Ensemble Methods/9. Additional Reading Resources.html 2KB
  185. 8. Cost Sensitive Learning/13. Additional Reading Resources.html 2KB
  186. 8. Cost Sensitive Learning/4. Cost Sensitive Approaches.srt 2KB
  187. 3. Evaluation Metrics/15. Additional reading resources (Optional).html 2KB
  188. 2. Machine Learning with Imbalanced Data Overview/4. Additional Reading Resources (Optional).html 1KB
  189. 1. Introduction/4. Code Jupyter notebooks.html 962B
  190. 9. Probability Calibration/11. Probability Additional reading resources.html 931B
  191. 10. Moving Forward/1. Next steps.html 712B
  192. 1. Introduction/6. Python package Imbalanced-learn.html 699B
  193. 3. Evaluation Metrics/5. Install Yellowbrick.html 684B
  194. 1. Introduction/7. Download Datasets.html 354B
  195. 1. Introduction/5. Presentations covered in the course.html 286B
  196. 3. Evaluation Metrics/16.1 Link to Jupyter notebook.html 177B
  197. 4. Udersampling/23. Summary Table.html 140B
  198. 0. Websites you may like/[FCS Forum].url 133B
  199. 0. Websites you may like/[FreeCourseSite.com].url 127B
  200. 0. Websites you may like/[CourseClub.ME].url 122B