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

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3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4 86.8 MB
3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.mp4 80.3 MB
8. Cost Sensitive Learning/9. Bayes Conditional Risk.mp4 72.0 MB
7. Ensemble Methods/8. Ensemble Methods - Demo.mp4 70.8 MB
7. Ensemble Methods/5. Boosting.mp4 70.6 MB
3. Evaluation Metrics/4. Precision, Recall and F-measure.mp4 67.0 MB
4. Udersampling/3. Random Under-Sampling - Demo.mp4 66.9 MB
9. Probability Calibration/3. Probability Calibration Curves - Demo.mp4 64.9 MB
8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.mp4 56.1 MB
4. Udersampling/5. Condensed Nearest Neighbours - Demo.mp4 52.7 MB
9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4 52.0 MB
3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.mp4 49.1 MB
9. Probability Calibration/5. Brier Score - Demo.mp4 49.0 MB
5. Oversampling/6. SMOTE-NC.mp4 48.0 MB
3. Evaluation Metrics/3. Accuracy - Demo.mp4 47.6 MB
4. Udersampling/22. Undersampling Method Comparison.mp4 47.5 MB
7. Ensemble Methods/6. Boosting plus Re-Sampling.mp4 47.3 MB
9. Probability Calibration/8. Calibrating a Classifier - Demo.mp4 46.7 MB
5. Oversampling/10. Borderline SMOTE.mp4 46.2 MB
5. Oversampling/4. SMOTE.mp4 44.6 MB
8. Cost Sensitive Learning/2. Types of Cost.mp4 44.0 MB
7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.mp4 42.9 MB
8. Cost Sensitive Learning/10. MetaCost.mp4 42.6 MB
3. Evaluation Metrics/13. Precision-Recall Curve.mp4 40.5 MB
5. Oversampling/16. Over-Sampling Method Comparison.mp4 39.8 MB
3. Evaluation Metrics/11. ROC-AUC.mp4 39.3 MB
5. Oversampling/13. SVM SMOTE - Demo.mp4 37.0 MB
8. Cost Sensitive Learning/12. Optional MetaCost Base Code.mp4 36.9 MB
6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.mp4 36.9 MB
6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.mp4 36.5 MB
5. Oversampling/3. Random Over-Sampling - Demo.mp4 35.2 MB
2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.mp4 35.1 MB
6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.mp4 34.3 MB
9. Probability Calibration/1. Probability Calibration.mp4 34.1 MB
2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.mp4 33.3 MB
8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.mp4 32.7 MB
4. Udersampling/4. Condensed Nearest Neighbours - Intro.mp4 32.4 MB
1. Introduction/1. Introduction.mp4 32.3 MB
5. Oversampling/8. ADASYN.mp4 31.6 MB
3. Evaluation Metrics/12. ROC-AUC - Demo.mp4 31.6 MB
4. Udersampling/1. Under-Sampling Methods - Introduction.mp4 31.5 MB
4. Udersampling/11. Edited Nearest Neighbours - Demo.mp4 30.8 MB
4. Udersampling/21. Instance Hardness Threshold - Demo.mp4 30.5 MB
7. Ensemble Methods/7. Hybdrid Methods.mp4 30.5 MB
3. Evaluation Metrics/7. Confusion tables, FPR and FNR.mp4 29.7 MB
9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4 29.6 MB
9. Probability Calibration/2. Probability Calibration Curves.mp4 28.8 MB
5. Oversampling/14. K-Means SMOTE.mp4 27.6 MB
9. Probability Calibration/7. Calibrating a Classifier.mp4 27.2 MB
7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.mp4 26.5 MB
4. Udersampling/19. NearMiss - Demo.mp4 26.3 MB
4. Udersampling/2. Random Under-Sampling - Intro.mp4 25.6 MB
4. Udersampling/9. One Sided Selection - Demo.mp4 25.6 MB
5. Oversampling/12. SVM SMOTE.mp4 25.3 MB
9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.mp4 25.2 MB
5. Oversampling/15. K-Means SMOTE - Demo.mp4 24.8 MB
5. Oversampling/11. Borderline SMOTE - Demo.mp4 24.8 MB
4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.mp4 24.3 MB
4. Udersampling/7. Tomek Links - Demo.mp4 24.0 MB
3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4 23.1 MB
4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.mp4 23.0 MB
8. Cost Sensitive Learning/11. MetaCost - Demo.mp4 22.9 MB
8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.mp4 22.9 MB
4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.mp4 22.9 MB
4. Udersampling/15. All KNN - Demo.mp4 22.7 MB
4. Udersampling/10. Edited Nearest Neighbours - Intro.mp4 22.6 MB
3. Evaluation Metrics/2. Accuracy.mp4 21.4 MB
5. Oversampling/7. SMOTE-NC - Demo.mp4 21.4 MB
8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.mp4 21.3 MB
5. Oversampling/1. Over-Sampling Methods - Introduction.mp4 21.1 MB
5. Oversampling/9. ADASYN - Demo.mp4 20.9 MB
3. Evaluation Metrics/16. Probability.mp4 20.6 MB
2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.mp4 20.2 MB
7. Ensemble Methods/2. Foundations of Ensemble Learning.mp4 19.7 MB
4. Udersampling/20. Instance Hardness Threshold - Intro.mp4 19.7 MB
4. Udersampling/6. Tomek Links - Intro.mp4 19.0 MB
8. Cost Sensitive Learning/3. Obtaining the Cost.mp4 19.0 MB
8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.mp4 18.7 MB
5. Oversampling/5. SMOTE - Demo.mp4 18.4 MB
7. Ensemble Methods/3. Bagging.mp4 18.2 MB
3. Evaluation Metrics/14. Precision-Recall Curve - Demo.mp4 18.1 MB
1. Introduction/2. Course Curriculum Overview.mp4 17.5 MB
4. Udersampling/18. NearMiss - Intro.mp4 17.2 MB
9. Probability Calibration/4. Brier Score.mp4 17.1 MB
4. Udersampling/14. All KNN - Intro.mp4 16.3 MB
4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.mp4 15.9 MB
5. Oversampling/2. Random Over-Sampling.mp4 15.6 MB
4. Udersampling/8. One Sided Selection - Intro.mp4 11.9 MB
1. Introduction/3. Course Material.mp4 11.0 MB
3. Evaluation Metrics/1. Introduction to Performance Metrics.mp4 10.8 MB
8. Cost Sensitive Learning/4. Cost Sensitive Approaches.mp4 10.3 MB
4. Udersampling/23.1 Undersampling-Comparison.pdf 205.5 KB
3. Evaluation Metrics/4. Precision, Recall and F-measure.srt 15.1 KB
8. Cost Sensitive Learning/9. Bayes Conditional Risk.srt 14.7 KB
4. Udersampling/3. Random Under-Sampling - Demo.srt 13.5 KB
3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt 12.2 KB
3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.srt 12.2 KB
8. Cost Sensitive Learning/2. Types of Cost.srt 12.1 KB
7. Ensemble Methods/8. Ensemble Methods - Demo.srt 11.8 KB
9. Probability Calibration/3. Probability Calibration Curves - Demo.srt 11.5 KB
7. Ensemble Methods/5. Boosting.srt 10.6 KB
5. Oversampling/6. SMOTE-NC.srt 10.4 KB
9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.srt 10.4 KB
5. Oversampling/4. SMOTE.srt 10.0 KB
3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.srt 9.6 KB
5. Oversampling/10. Borderline SMOTE.srt 9.3 KB
4. Udersampling/22. Undersampling Method Comparison.srt 9.3 KB
3. Evaluation Metrics/13. Precision-Recall Curve.srt 9.2 KB
4. Udersampling/5. Condensed Nearest Neighbours - Demo.srt 9.2 KB
8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.srt 9.0 KB
9. Probability Calibration/5. Brier Score - Demo.srt 8.8 KB
8. Cost Sensitive Learning/10. MetaCost.srt 8.5 KB
3. Evaluation Metrics/11. ROC-AUC.srt 8.3 KB
4. Udersampling/4. Condensed Nearest Neighbours - Intro.srt 8.3 KB
7. Ensemble Methods/6. Boosting plus Re-Sampling.srt 8.0 KB
8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.srt 7.8 KB
5. Oversampling/8. ADASYN.srt 7.7 KB
8. Cost Sensitive Learning/12. Optional MetaCost Base Code.srt 7.5 KB
3. Evaluation Metrics/7. Confusion tables, FPR and FNR.srt 7.4 KB
9. Probability Calibration/1. Probability Calibration.srt 7.3 KB
3. Evaluation Metrics/3. Accuracy - Demo.srt 7.3 KB
9. Probability Calibration/8. Calibrating a Classifier - Demo.srt 7.3 KB
6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.srt 7.3 KB
5. Oversampling/16. Over-Sampling Method Comparison.srt 7.2 KB
9. Probability Calibration/2. Probability Calibration Curves.srt 6.7 KB
4. Udersampling/2. Random Under-Sampling - Intro.srt 6.6 KB
4. Udersampling/1. Under-Sampling Methods - Introduction.srt 6.6 KB
6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.srt 6.5 KB
2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.srt 6.5 KB
7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.srt 6.4 KB
5. Oversampling/3. Random Over-Sampling - Demo.srt 6.3 KB
6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.srt 6.3 KB
9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt 6.2 KB
5. Oversampling/12. SVM SMOTE.srt 6.1 KB
5. Oversampling/14. K-Means SMOTE.srt 6.0 KB
2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.srt 5.9 KB
9. Probability Calibration/7. Calibrating a Classifier.srt 5.9 KB
3. Evaluation Metrics/16. Probability.srt 5.5 KB
4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.srt 5.4 KB
7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.srt 5.4 KB
4. Udersampling/10. Edited Nearest Neighbours - Intro.srt 5.4 KB
3. Evaluation Metrics/12. ROC-AUC - Demo.srt 5.3 KB
3. Evaluation Metrics/2. Accuracy.srt 5.3 KB
7. Ensemble Methods/7. Hybdrid Methods.srt 5.3 KB
4. Udersampling/6. Tomek Links - Intro.srt 5.3 KB
3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt 5.2 KB
4. Udersampling/11. Edited Nearest Neighbours - Demo.srt 5.1 KB
4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.srt 5.0 KB
4. Udersampling/20. Instance Hardness Threshold - Intro.srt 5.0 KB
5. Oversampling/13. SVM SMOTE - Demo.srt 4.9 KB
4. Udersampling/21. Instance Hardness Threshold - Demo.srt 4.8 KB
2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.srt 4.7 KB
4. Udersampling/9. One Sided Selection - Demo.srt 4.7 KB
9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.srt 4.6 KB
8. Cost Sensitive Learning/3. Obtaining the Cost.srt 4.6 KB
4. Udersampling/19. NearMiss - Demo.srt 4.5 KB
8. Cost Sensitive Learning/11. MetaCost - Demo.srt 4.5 KB
4. Udersampling/18. NearMiss - Intro.srt 4.4 KB
8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.srt 4.4 KB
5. Oversampling/1. Over-Sampling Methods - Introduction.srt 4.4 KB
4. Udersampling/14. All KNN - Intro.srt 4.3 KB
8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.srt 4.1 KB
4. Udersampling/7. Tomek Links - Demo.srt 4.1 KB
1. Introduction/1. Introduction.srt 4.0 KB
4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.srt 3.9 KB
5. Oversampling/15. K-Means SMOTE - Demo.srt 3.9 KB
1. Introduction/2. Course Curriculum Overview.srt 3.9 KB
5. Oversampling/9. ADASYN - Demo.srt 3.7 KB
5. Oversampling/2. Random Over-Sampling.srt 3.7 KB
9. Probability Calibration/4. Brier Score.srt 3.7 KB
8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.srt 3.6 KB
5. Oversampling/11. Borderline SMOTE - Demo.srt 3.6 KB
4. Udersampling/15. All KNN - Demo.srt 3.6 KB
3. Evaluation Metrics/14. Precision-Recall Curve - Demo.srt 3.4 KB
5. Oversampling/7. SMOTE-NC - Demo.srt 3.3 KB
3. Evaluation Metrics/1. Introduction to Performance Metrics.srt 3.3 KB
7. Ensemble Methods/3. Bagging.srt 3.2 KB
7. Ensemble Methods/2. Foundations of Ensemble Learning.srt 3.2 KB
5. Oversampling/5. SMOTE - Demo.srt 3.2 KB
4. Udersampling/8. One Sided Selection - Intro.srt 2.8 KB
4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.srt 2.6 KB
1. Introduction/8. Additional resources for Machine Learning and Python programming.html 2.6 KB
1. Introduction/3. Course Material.srt 2.4 KB
7. Ensemble Methods/9. Additional Reading Resources.html 2.0 KB
8. Cost Sensitive Learning/13. Additional Reading Resources.html 2.0 KB
8. Cost Sensitive Learning/4. Cost Sensitive Approaches.srt 1.8 KB
3. Evaluation Metrics/15. Additional reading resources (Optional).html 1.6 KB
2. Machine Learning with Imbalanced Data Overview/4. Additional Reading Resources (Optional).html 1.0 KB
1. Introduction/4. Code Jupyter notebooks.html 962 bytes
9. Probability Calibration/11. Probability Additional reading resources.html 931 bytes
10. Moving Forward/1. Next steps.html 712 bytes
1. Introduction/6. Python package Imbalanced-learn.html 699 bytes
3. Evaluation Metrics/5. Install Yellowbrick.html 684 bytes
1. Introduction/7. Download Datasets.html 354 bytes
1. Introduction/5. Presentations covered in the course.html 286 bytes
3. Evaluation Metrics/16.1 Link to Jupyter notebook.html 177 bytes
4. Udersampling/23. Summary Table.html 140 bytes
0. Websites you may like/[FCS Forum].url 133 bytes
0. Websites you may like/[FreeCourseSite.com].url 127 bytes
0. Websites you may like/[CourseClub.ME].url 122 bytes

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