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[FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data

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视频 2021-8-10 11:41 2025-1-13 04:41 124 2.94 GB 91
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FreeCourseSite com Udemy - Machine Learning with Imbalanced Data

文件列表
  1. 1. Introduction/1. Introduction.mp432.25MB
  2. 1. Introduction/2. Course Curriculum Overview.mp417.54MB
  3. 1. Introduction/3. Course Material.mp410.96MB
  4. 2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.mp433.3MB
  5. 2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.mp435.11MB
  6. 2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.mp420.24MB
  7. 3. Evaluation Metrics/1. Introduction to Performance Metrics.mp410.79MB
  8. 3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp486.77MB
  9. 3. Evaluation Metrics/11. ROC-AUC.mp439.25MB
  10. 3. Evaluation Metrics/12. ROC-AUC - Demo.mp431.56MB
  11. 3. Evaluation Metrics/13. Precision-Recall Curve.mp440.5MB
  12. 3. Evaluation Metrics/14. Precision-Recall Curve - Demo.mp418.08MB
  13. 3. Evaluation Metrics/16. Probability.mp420.64MB
  14. 3. Evaluation Metrics/2. Accuracy.mp421.44MB
  15. 3. Evaluation Metrics/3. Accuracy - Demo.mp447.61MB
  16. 3. Evaluation Metrics/4. Precision, Recall and F-measure.mp466.98MB
  17. 3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.mp480.33MB
  18. 3. Evaluation Metrics/7. Confusion tables, FPR and FNR.mp429.72MB
  19. 3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.mp449.08MB
  20. 3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp423.06MB
  21. 4. Udersampling/1. Under-Sampling Methods - Introduction.mp431.45MB
  22. 4. Udersampling/10. Edited Nearest Neighbours - Intro.mp422.57MB
  23. 4. Udersampling/11. Edited Nearest Neighbours - Demo.mp430.82MB
  24. 4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.mp424.27MB
  25. 4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.mp422.89MB
  26. 4. Udersampling/14. All KNN - Intro.mp416.27MB
  27. 4. Udersampling/15. All KNN - Demo.mp422.65MB
  28. 4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.mp423.04MB
  29. 4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.mp415.9MB
  30. 4. Udersampling/18. NearMiss - Intro.mp417.18MB
  31. 4. Udersampling/19. NearMiss - Demo.mp426.33MB
  32. 4. Udersampling/2. Random Under-Sampling - Intro.mp425.62MB
  33. 4. Udersampling/20. Instance Hardness Threshold - Intro.mp419.7MB
  34. 4. Udersampling/21. Instance Hardness Threshold - Demo.mp430.54MB
  35. 4. Udersampling/22. Undersampling Method Comparison.mp447.52MB
  36. 4. Udersampling/3. Random Under-Sampling - Demo.mp466.91MB
  37. 4. Udersampling/4. Condensed Nearest Neighbours - Intro.mp432.43MB
  38. 4. Udersampling/5. Condensed Nearest Neighbours - Demo.mp452.71MB
  39. 4. Udersampling/6. Tomek Links - Intro.mp418.97MB
  40. 4. Udersampling/7. Tomek Links - Demo.mp423.98MB
  41. 4. Udersampling/8. One Sided Selection - Intro.mp411.9MB
  42. 4. Udersampling/9. One Sided Selection - Demo.mp425.59MB
  43. 5. Oversampling/1. Over-Sampling Methods - Introduction.mp421.09MB
  44. 5. Oversampling/10. Borderline SMOTE.mp446.2MB
  45. 5. Oversampling/11. Borderline SMOTE - Demo.mp424.77MB
  46. 5. Oversampling/12. SVM SMOTE.mp425.27MB
  47. 5. Oversampling/13. SVM SMOTE - Demo.mp437.01MB
  48. 5. Oversampling/14. K-Means SMOTE.mp427.6MB
  49. 5. Oversampling/15. K-Means SMOTE - Demo.mp424.77MB
  50. 5. Oversampling/16. Over-Sampling Method Comparison.mp439.77MB
  51. 5. Oversampling/2. Random Over-Sampling.mp415.65MB
  52. 5. Oversampling/3. Random Over-Sampling - Demo.mp435.2MB
  53. 5. Oversampling/4. SMOTE.mp444.61MB
  54. 5. Oversampling/5. SMOTE - Demo.mp418.38MB
  55. 5. Oversampling/6. SMOTE-NC.mp448.03MB
  56. 5. Oversampling/7. SMOTE-NC - Demo.mp421.43MB
  57. 5. Oversampling/8. ADASYN.mp431.6MB
  58. 5. Oversampling/9. ADASYN - Demo.mp420.95MB
  59. 6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.mp436.9MB
  60. 6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.mp434.33MB
  61. 6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.mp436.54MB
  62. 7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.mp426.54MB
  63. 7. Ensemble Methods/2. Foundations of Ensemble Learning.mp419.71MB
  64. 7. Ensemble Methods/3. Bagging.mp418.19MB
  65. 7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.mp442.87MB
  66. 7. Ensemble Methods/5. Boosting.mp470.58MB
  67. 7. Ensemble Methods/6. Boosting plus Re-Sampling.mp447.31MB
  68. 7. Ensemble Methods/7. Hybdrid Methods.mp430.49MB
  69. 7. Ensemble Methods/8. Ensemble Methods - Demo.mp470.85MB
  70. 8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.mp432.73MB
  71. 8. Cost Sensitive Learning/10. MetaCost.mp442.57MB
  72. 8. Cost Sensitive Learning/11. MetaCost - Demo.mp422.94MB
  73. 8. Cost Sensitive Learning/12. Optional MetaCost Base Code.mp436.92MB
  74. 8. Cost Sensitive Learning/2. Types of Cost.mp443.99MB
  75. 8. Cost Sensitive Learning/3. Obtaining the Cost.mp418.96MB
  76. 8. Cost Sensitive Learning/4. Cost Sensitive Approaches.mp410.33MB
  77. 8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.mp418.69MB
  78. 8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.mp421.26MB
  79. 8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.mp456.06MB
  80. 8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.mp422.9MB
  81. 8. Cost Sensitive Learning/9. Bayes Conditional Risk.mp472.04MB
  82. 9. Probability Calibration/1. Probability Calibration.mp434.09MB
  83. 9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.mp425.19MB
  84. 9. Probability Calibration/2. Probability Calibration Curves.mp428.76MB
  85. 9. Probability Calibration/3. Probability Calibration Curves - Demo.mp464.88MB
  86. 9. Probability Calibration/4. Brier Score.mp417.15MB
  87. 9. Probability Calibration/5. Brier Score - Demo.mp449.02MB
  88. 9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp429.58MB
  89. 9. Probability Calibration/7. Calibrating a Classifier.mp427.19MB
  90. 9. Probability Calibration/8. Calibrating a Classifier - Demo.mp446.73MB
  91. 9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.mp452MB
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