Udemy - Machine Learning with Imbalanced Data [FCS]

Files:

• [CourseClub.ME].url (0.1 KB)
• [FCS Forum].url (0.1 KB)
• [FreeCourseSite.com].url (0.1 KB)

• 1. Introduction.mp4 (32.3 MB)
• 1. Introduction.srt (4.0 KB)
• 2. Course Curriculum Overview.mp4 (17.5 MB)
• 2. Course Curriculum Overview.srt (3.9 KB)
• 3. Course Material.mp4 (11.0 MB)
• 3. Course Material.srt (2.4 KB)
• 4. Code Jupyter notebooks.html (0.9 KB)
• 5. Presentations covered in the course.html (0.3 KB)
• 6. Python package Imbalanced-learn.html (0.7 KB)
• 7. Download Datasets.html (0.3 KB)
• 8. Additional resources for Machine Learning and Python programming.html (2.6 KB)

• 1. Next steps.html (0.7 KB)

• 1. Imbalanced classes - Introduction.mp4 (33.3 MB)
• 1. Imbalanced classes - Introduction.srt (6.5 KB)
• 2. Nature of the imbalanced class.mp4 (35.1 MB)
• 2. Nature of the imbalanced class.srt (5.9 KB)
• 3. Approaches to work with imbalanced datasets - Overview.mp4 (20.2 MB)
• 3. Approaches to work with imbalanced datasets - Overview.srt (4.7 KB)
• 4. Additional Reading Resources (Optional).html (1.0 KB)

• 1. Introduction to Performance Metrics.mp4 (10.8 MB)
• 1. Introduction to Performance Metrics.srt (3.3 KB)
• 10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4 (86.8 MB)
• 10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt (12.2 KB)
• 11. ROC-AUC.mp4 (39.3 MB)
• 11. ROC-AUC.srt (8.3 KB)
• 12. ROC-AUC - Demo.mp4 (31.6 MB)
• 12. ROC-AUC - Demo.srt (5.3 KB)
• 13. Precision-Recall Curve.mp4 (40.5 MB)
• 13. Precision-Recall Curve.srt (9.2 KB)
• 14. Precision-Recall Curve - Demo.mp4 (18.1 MB)
• 14. Precision-Recall Curve - Demo.srt (3.4 KB)
• 15. Additional reading resources (Optional).html (1.6 KB)
• 16. Probability.mp4 (20.6 MB)
• 16. Probability.srt (5.5 KB)
• 16.1 Link to Jupyter notebook.html (0.2 KB)
• 2. Accuracy.mp4 (21.4 MB)
• 2. Accuracy.srt (5.3 KB)
• 3. Accuracy - Demo.mp4 (47.6 MB)
• 3. Accuracy - Demo.srt (7.3 KB)
• 4. Precision, Recall and F-measure.mp4 (67.0 MB)
• 4. Precision, Recall and F-measure.srt (15.1 KB)
• 5. Install Yellowbrick.html (0.7 KB)
• 6. Precision, Recall and F-measure - Demo.mp4 (80.3 MB)
• 6. Precision, Recall and F-measure - Demo.srt (12.2 KB)
• 7. Confusion tables, FPR and FNR.mp4 (29.7 MB)
• 7. Confusion tables, FPR and FNR.srt (7.4 KB)
• 8. Confusion tables, FPR and FNR - Demo.mp4 (49.1 MB)
• 8. Confusion tables, FPR and FNR - Demo.srt (9.6 KB)
• 9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4 (23.1 MB)
• 9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt (5.2 KB)

• 1. Under-Sampling Methods - Introduction.mp4 (31.5 MB)
• 1. Under-Sampling Methods - Introduction.srt (6.6 KB)
• 10. Edited Nearest Neighbours - Intro.mp4 (22.6 MB)
• 10. Edited Nearest Neighbours - Intro.srt (5.4 KB)
• 11. Edited Nearest Neighbours - Demo.mp4 (30.8 MB)
• 11. Edited Nearest Neighbours - Demo.srt (5.1 KB)
• 12. Repeated Edited Nearest Neighbours - Intro.mp4 (24.3 MB)
• 12. Repeated Edited Nearest Neighbours - Intro.srt (5.4 KB)
• 13. Repeated Edited Nearest Neighbours - Demo.mp4 (22.9 MB)
• 13. Repeated Edited Nearest Neighbours - Demo.srt (3.9 KB)
• 14. All KNN - Intro.mp4 (16.3 MB)
• 14. All KNN - Intro.srt (4.3 KB)
• 15. All KNN - Demo.mp4 (22.7 MB)
• 15. All KNN - Demo.srt (3.6 KB)
• 16. Neighbourhood Cleaning Rule - Intro.mp4 (23.0 MB)
• 16. Neighbourhood Cleaning Rule - Intro.srt (5.0 KB)
• 17. Neighbourhood Cleaning Rule - Demo.mp4 (15.9 MB)
• 17. Neighbourhood Cleaning Rule - Demo.srt (2.6 KB)
• 18. NearMiss - Intro.mp4 (17.2 MB)
• 18. NearMiss - Intro.srt (4.4 KB)
• 19. NearMiss - Demo.mp4 (26.3 MB)
• 19. NearMiss - Demo.srt (4.5 KB)
• 2. Random Under-Sampling - Intro.mp4 (25.6 MB)
• 2. Random Under-Sampling - Intro.srt (6.6 KB)
• 20. Instance Hardness Threshold - Intro.mp4 (19.7 MB)
• 20. Instance Hardness Threshold - Intro.srt (5.0 KB)
• 21. Instance Hardness Threshold - Demo.mp4 (30.5 MB)
• 21. Instance Hardness Threshold - Demo.srt (4.8 KB)
• 22. Undersampling Method Comparison.mp4 (47.5 MB)
• 22. Undersampling Method Comparison.srt (9.3 KB)
• 23. Summary Table.html (0.1 KB)
• 23.1 Undersampling-Comparison.pdf (205.5 KB)
• 3. Random Under-Sampling - Demo.mp4 (66.9 MB)
• 3. Random Under-Sampling - Demo.srt (13.5 KB)
• 4. Condensed Nearest Neighbours - Intro.mp4 (32.4 MB)
• 4. Condensed Nearest Neighbours - Intro.srt (8.3 KB)
• 5. Condensed Nearest Neighbours - Demo.mp4 (52.7 MB)
• 5. Condensed Nearest Neighbours - Demo.srt (9.2 KB)
• 6. Tomek Links - Intro.mp4 (19.0 MB)
• 6. Tomek Links - Intro.srt (5.3 KB)
• 7. Tomek Links - Demo.mp4 (24.0 MB)
• 7. Tomek Links - Demo.srt (4.1 KB)
• 8. One Sided Selection - Intro.mp4 (11.9 MB)
• 8. One Sided Selection - Intro.srt (2.8 KB)
• 9. One Sided Selection - Demo.mp4 (25.6 MB)
• 9. One Sided Selection - Demo.srt (4.7 KB)

• 1. Over-Sampling Methods - Introduction.mp4 (21.1 MB)
• 1. Over-Sampling Methods - Introduction.srt (4.4 KB)
• 10. Borderline SMOTE.mp4 (46.2 MB)
• 10. Borderline SMOTE.srt (9.3 KB)
• 11. Borderline SMOTE - Demo.mp4 (24.8 MB)
• 11. Borderline SMOTE - Demo.srt (3.6 KB)
• 12. SVM SMOTE.mp4 (25.3 MB)
• 12. SVM SMOTE.srt (6.1 KB)
• 13. SVM SMOTE - Demo.mp4 (37.0 MB)
• 13. SVM SMOTE - Demo.srt (4.9 KB)
• 14. K-Means SMOTE.mp4 (27.6 MB)
• 14. K-Means SMOTE.srt (6.0 KB)
• 15. K-Means SMOTE - Demo.mp4 (24.8 MB)
• 15. K-Means SMOTE - Demo.srt (3.9 KB)
• 16. Over-Sampling Method Comparison.mp4 (39.8 MB)
• 16. Over-Sampling Method Comparison.srt (7.2 KB)
• 2. Random Over-Sampling.mp4 (15.6 MB)
• 2. Random Over-Sampling.srt (3.7 KB)
• 3. Random Over-Sampling - Demo.mp4 (35.2 MB)
• 3. Random Over-Sampling - Demo.srt (6.3 KB)
• 4. SMOTE.mp4 (44.6 MB)
• 4. SMOTE.srt (10.0 KB)
• 5. SMOTE - Demo.mp4 (18.4 MB)
• 5. SMOTE - Demo.srt (3.2 KB)
• 6. SMOTE-NC.mp4 (48.0 MB)
• 6. SMOTE-NC.srt (10.4 KB)
• 7. SMOTE-NC - Demo.mp4 (21.4 MB)
• 7. SMOTE-NC - Demo.srt (3.3 KB)
• 8. ADASYN.mp4 (31.6 MB)
• 8. ADASYN.srt (7.7 KB)
• 9. ADASYN - Demo.mp4 (20.9 MB)
• 9. ADASYN - Demo.srt (3.7 KB)

• 1. Combining Over and Under-sampling - Intro.mp4 (36.9 MB)
• 1. Combining Over and Under-sampling - Intro.srt (7.3 KB)
• 2. Combining Over and Under-sampling - Demo.mp4 (34.3 MB)
• 2. Combining Over and Under-sampling - Demo.srt (6.3 KB)
• 3. Comparison of Over and Under-sampling Methods.mp4 (36.5 MB)
• 3. Comparison of Over and Under-sampling Methods.srt (6.5 KB)

• 1. Ensemble methods with Imbalanced Data.mp4 (26.5 MB)
• 1. Ensemble methods with Imbalanced Data.srt (5.4 KB)
• 2. Foundations of Ensemble Learning.mp4 (19.7 MB)
• 2. Foundations of Ensemble Learning.srt (3.2 KB)
• 3. Bagging.mp4 (18.2 MB)
• 3. Bagging.srt (3.2 KB)
• 4. Bagging plus Over- or Under-Sampling.mp4 (42.9 MB)
• 4. Bagging plus Over- or Under-Sampling.srt (6.4 KB)
• 5. Boosting.mp4 (70.6 MB)
• 5. Boosting.srt (10.6 KB)
• 6. Boosting plus Re-Sampling.mp4 (47.3 MB)
• 6. Boosting plus Re-Sampling.srt (8.0 KB)
• 7. Hybdrid Methods.mp4 (30.5 MB)
• 7. Hybdrid Methods.srt (5.3 KB)
• 8. Ensemble Methods - Demo.mp4 (70.8 MB)
• 8. Ensemble Methods - Demo.srt (11.8 KB)
• 9. Additional Reading Resources.html (2.0 KB)

• 1. Cost-sensitive Learning - Intro.mp4 (32.7 MB)
• 1. Cost-sensitive Learning - Intro.srt (7.8 KB)
• 10. MetaCost.mp4 (42.6 MB)
• 10. MetaCost.srt (8.5 KB)
• 11. MetaCost - Demo.mp4 (22.9 MB)
• 11. MetaCost - Demo.srt (4.5 KB)
• 12. Optional MetaCost Base Code.mp4 (36.9 MB)
• 12. Optional MetaCost Base Code.srt (7.5 KB)
• 13. Additional Reading Resources.html (2.0 KB)
• 2. Types of Cost.mp4 (44.0 MB)
• 2. Types of Cost.srt (12.1 KB)
• 3. Obtaining the Cost.mp4 (19.0 MB)
• 3. Obtaining the Cost.srt (4.6 KB)
• 4. Cost Sensitive Approaches.mp4 (10.3 MB)
• 4. Cost Sensitive Approaches.srt (1.8 KB)
• 5. Misclassification Cost in Logistic Regression.mp4 (18.7 MB)
• 5. Misclassification Cost in Logistic Regression.srt (3.6 KB)
• 6. Misclassification Cost in Decision Trees.mp4 (21.3 MB)
• 6. Misclassification Cost in Decision Trees.srt (4.1 KB)
• 7. Cost Sensitive Learning with Scikit-learn- Demo.mp4 (56.1 MB)
• 7. Cost Sensitive Learning with Scikit-learn- Demo.srt (9.0 KB)
• 8. Find Optimal Cost with hyperparameter tuning.mp4 (22.9 MB)
• 8. Find Optimal Cost with hyperparameter tuning.srt (4.4 KB)
• 9. Bayes Conditional Risk.mp4 (72.0 MB)
• 9. Bayes Conditional Risk.srt (14.7 KB)

• 1. Probability Calibration.mp4 (34.1 MB)
• 1. Probability Calibration.srt (7.3 KB)
• 10. Calibrating a Classifier with Cost-sensitive Learning.mp4 (25.2 MB)
• 10. Calibrating a Classifier with Cost-sensitive Learning.srt (4.6 KB)
• 11. Probability Additional reading resources.html (0.9 KB)
• 2. Probability Calibration Curves.mp4 (28.8 MB)
• 2. Probability Calibration Curves.srt (6.7 KB)
• 3. Probability Calibration Curves - Demo.mp4 (64.9 MB)
• 3. Probability Calibration Curves - Demo.srt (11.5 KB)
• 4. Brier Score.mp4 (17.1 MB)
• 4. Brier Score.srt (3.7 KB)
• 5. Brier Score - Demo.mp4 (49.0 MB)
• 5. Brier Score - Demo.srt (8.8 KB)
• 6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4 (29.6 MB)
• 6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt (6.2 KB)
• 7. Calibrating a Classifier.mp4 (27.2 MB)
• 7. Calibrating a Classifier.srt (5.9 KB)
• 8. Calibrating a Classifier - Demo.mp4 (46.7 MB)
• 8. Calibrating a Classifier - Demo.srt (7.3 KB)
• 9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4 (52.0 MB)
• 9. Calibrating a Classfiier after SMOTE or Under-sampling.srt (10.4 KB)