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Machine Learning with Imbalanced Data
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[TutsNode.com] - Machine Learning with Imbalanced Data (Size: 2.99 GB) (Files: 289)
[TutsNode.com] - Machine Learning with Imbalanced Data
3. Evaluation Metrics
10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4
4. Precision, Recall and F-measure.srt
16.1 Link to Jupyter notebook.html
6. Precision, Recall and F-measure - Demo.mp4
10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt
6. Precision, Recall and F-measure - Demo.srt
8. Confusion tables, FPR and FNR - Demo.srt
13. Precision-Recall Curve.srt
5. Install Yellowbrick.html
11. ROC-AUC.srt
7. Confusion tables, FPR and FNR.srt
3. Accuracy - Demo.srt
15. Additional reading resources (Optional).html
16. Probability.srt
12. ROC-AUC - Demo.srt
2. Accuracy.srt
9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt
14. Precision-Recall Curve - Demo.srt
1. Introduction to Performance Metrics.srt
4. Precision, Recall and F-measure.mp4
8. Confusion tables, FPR and FNR - Demo.mp4
3. Accuracy - Demo.mp4
13. Precision-Recall Curve.mp4
11. ROC-AUC.mp4
12. ROC-AUC - Demo.mp4
7. Confusion tables, FPR and FNR.mp4
9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4
2. Accuracy.mp4
16. Probability.mp4
14. Precision-Recall Curve - Demo.mp4
1. Introduction to Performance Metrics.mp4
4. Udersampling
23.1 Undersampling-Comparison.pdf
3. Random Under-Sampling - Demo.srt
22. Undersampling Method Comparison.srt
5. Condensed Nearest Neighbours - Demo.srt
4. Condensed Nearest Neighbours - Intro.srt
2. Random Under-Sampling - Intro.srt
1. Under-Sampling Methods - Introduction.srt
12. Repeated Edited Nearest Neighbours - Intro.srt
10. Edited Nearest Neighbours - Intro.srt
8. One Sided Selection - Intro.srt
6. Tomek Links - Intro.srt
11. Edited Nearest Neighbours - Demo.srt
16. Neighbourhood Cleaning Rule - Intro.srt
20. Instance Hardness Threshold - Intro.srt
21. Instance Hardness Threshold - Demo.srt
9. One Sided Selection - Demo.srt
19. NearMiss - Demo.srt
18. NearMiss - Intro.srt
17. Neighbourhood Cleaning Rule - Demo.srt
14. All KNN - Intro.srt
7. Tomek Links - Demo.srt
13. Repeated Edited Nearest Neighbours - Demo.srt
23. Summary Table.html
15. All KNN - Demo.srt
3. Random Under-Sampling - Demo.mp4
5. Condensed Nearest Neighbours - Demo.mp4
22. Undersampling Method Comparison.mp4
4. Condensed Nearest Neighbours - Intro.mp4
1. Under-Sampling Methods - Introduction.mp4
11. Edited Nearest Neighbours - Demo.mp4
21. Instance Hardness Threshold - Demo.mp4
19. NearMiss - Demo.mp4
2. Random Under-Sampling - Intro.mp4
9. One Sided Selection - Demo.mp4
12. Repeated Edited Nearest Neighbours - Intro.mp4
7. Tomek Links - Demo.mp4
16. Neighbourhood Cleaning Rule - Intro.mp4
13. Repeated Edited Nearest Neighbours - Demo.mp4
15. All KNN - Demo.mp4
10. Edited Nearest Neighbours - Intro.mp4
20. Instance Hardness Threshold - Intro.mp4
6. Tomek Links - Intro.mp4
18. NearMiss - Intro.mp4
14. All KNN - Intro.mp4
17. Neighbourhood Cleaning Rule - Demo.mp4
8. One Sided Selection - Intro.mp4
8. Cost Sensitive Learning
9. Bayes Conditional Risk.srt
2. Types of Cost.srt
7. Cost Sensitive Learning with Scikit-learn- Demo.srt
10. MetaCost.srt
1. Cost-sensitive Learning - Intro.srt
12. Optional MetaCost Base Code.srt
3. Obtaining the Cost.srt
11. MetaCost - Demo.srt
8. Find Optimal Cost with hyperparameter tuning.srt
6. Misclassification Cost in Decision Trees.srt
5. Misclassification Cost in Logistic Regression.srt
13. Additional Reading Resources.html
4. Cost Sensitive Approaches.srt
9. Bayes Conditional Risk.mp4
7. Cost Sensitive Learning with Scikit-learn- Demo.mp4
2. Types of Cost.mp4
10. MetaCost.mp4
12. Optional MetaCost Base Code.mp4
1. Cost-sensitive Learning - Intro.mp4
11. MetaCost - Demo.mp4
8. Find Optimal Cost with hyperparameter tuning.mp4
6. Misclassification Cost in Decision Trees.mp4
3. Obtaining the Cost.mp4
5. Misclassification Cost in Logistic Regression.mp4
4. Cost Sensitive Approaches.mp4
1. Introduction
4. Code Jupyter notebooks.html
5. Presentations covered in the course.html
6. Python package Imbalanced-learn.html
7. Download Datasets.html
8. Additional resources for Machine Learning and Python programming.html
3. Course Material.srt
1. Introduction.srt
2. Course Curriculum Overview.srt
1. Introduction.mp4
2. Course Curriculum Overview.mp4
3. Course Material.mp4
7. Ensemble Methods
8. Ensemble Methods - Demo.srt
5. Boosting.srt
6. Boosting plus Re-Sampling.srt
4. Bagging plus Over- or Under-Sampling.srt
1. Ensemble methods with Imbalanced Data.srt
7. Hybdrid Methods.srt
3. Bagging.srt
2. Foundations of Ensemble Learning.srt
9. Additional Reading Resources.html
8. Ensemble Methods - Demo.mp4
5. Boosting.mp4
6. Boosting plus Re-Sampling.mp4
4. Bagging plus Over- or Under-Sampling.mp4
7. Hybdrid Methods.mp4
1. Ensemble methods with Imbalanced Data.mp4
2. Foundations of Ensemble Learning.mp4
3. Bagging.mp4
9. Probability Calibration
3. Probability Calibration Curves - Demo.srt
9. Calibrating a Classfiier after SMOTE or Under-sampling.srt
5. Brier Score - Demo.srt
1. Probability Calibration.srt
8. Calibrating a Classifier - Demo.srt
2. Probability Calibration Curves.srt
6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt
7. Calibrating a Classifier.srt
10. Calibrating a Classifier with Cost-sensitive Learning.srt
4. Brier Score.srt
11. Probability Additional reading resources.html
3. Probability Calibration Curves - Demo.mp4
9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4
5. Brier Score - Demo.mp4
8. Calibrating a Classifier - Demo.mp4
1. Probability Calibration.mp4
6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4
2. Probability Calibration Curves.mp4
7. Calibrating a Classifier.mp4
10. Calibrating a Classifier with Cost-sensitive Learning.mp4
4. Brier Score.mp4
2. Machine Learning with Imbalanced Data Overview
4. Additional Reading Resources (Optional).html
1. Imbalanced classes - Introduction.srt
2. Nature of the imbalanced class.srt
3. Approaches to work with imbalanced datasets - Overview.srt
2. Nature of the imbalanced class.mp4
1. Imbalanced classes - Introduction.mp4
3. Approaches to work with imbalanced datasets - Overview.mp4
5. Oversampling
6. SMOTE-NC.srt
4. SMOTE.srt
10. Borderline SMOTE.srt
8. ADASYN.srt
16. Over-Sampling Method Comparison.srt
3. Random Over-Sampling - Demo.srt
12. SVM SMOTE.srt
14. K-Means SMOTE.srt
13. SVM SMOTE - Demo.srt
1. Over-Sampling Methods - Introduction.srt
15. K-Means SMOTE - Demo.srt
9. ADASYN - Demo.srt
2. Random Over-Sampling.srt
11. Borderline SMOTE - Demo.srt
7. SMOTE-NC - Demo.srt
5. SMOTE - Demo.srt
6. SMOTE-NC.mp4
10. Borderline SMOTE.mp4
4. SMOTE.mp4
16. Over-Sampling Method Comparison.mp4
13. SVM SMOTE - Demo.mp4
3. Random Over-Sampling - Demo.mp4
8. ADASYN.mp4
14. K-Means SMOTE.mp4
12. SVM SMOTE.mp4
15. K-Means SMOTE - Demo.mp4
11. Borderline SMOTE - Demo.mp4
7. SMOTE-NC - Demo.mp4
1. Over-Sampling Methods - Introduction.mp4
9. ADASYN - Demo.mp4
5. SMOTE - Demo.mp4
2. Random Over-Sampling.mp4
6. Over and Undersampling
1. Combining Over and Under-sampling - Intro.srt
3. Comparison of Over and Under-sampling Methods.srt
2. Combining Over and Under-sampling - Demo.srt
1. Combining Over and Under-sampling - Intro.mp4
3. Comparison of Over and Under-sampling Methods.mp4
2. Combining Over and Under-sampling - Demo.mp4
10. Moving Forward
1. Next steps.html
TutsNode.com.txt
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