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1. Next steps.html
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1. Under-Sampling Methods - Introduction.mp4
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10. Borderline SMOTE.mp4
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10. Calibrating a Classifier with Cost-sensitive Learning.mp4
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10. Edited Nearest Neighbours - Intro.mp4
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10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4
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10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt
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10. MetaCost.mp4
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11. Borderline SMOTE - Demo.mp4
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11. Edited Nearest Neighbours - Demo.mp4
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11. MetaCost - Demo.srt
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11. Probability Additional reading resources.html
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11. ROC-AUC.mp4
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12. Repeated Edited Nearest Neighbours - Intro.mp4
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12. SVM SMOTE.mp4
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12. SVM SMOTE.srt
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13. Additional Reading Resources.html
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13. Precision-Recall Curve.mp4
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13. Repeated Edited Nearest Neighbours - Demo.mp4
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13. SVM SMOTE - Demo.mp4
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14. All KNN - Intro.mp4
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14. K-Means SMOTE.mp4
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14. K-Means SMOTE.srt
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14. Precision-Recall Curve - Demo.mp4
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14. Precision-Recall Curve - Demo.srt
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15. Additional reading resources (Optional).html
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15. All KNN - Demo.mp4
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15. K-Means SMOTE - Demo.mp4
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16. Neighbourhood Cleaning Rule - Intro.mp4
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16. Over-Sampling Method Comparison.mp4
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16. Over-Sampling Method Comparison.srt
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16. Probability.mp4
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16. Probability.srt
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16.1 Link to Jupyter notebook.html
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17. Neighbourhood Cleaning Rule - Demo.mp4
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17. Neighbourhood Cleaning Rule - Demo.srt
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18. NearMiss - Intro.mp4
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19. NearMiss - Demo.mp4
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2. Accuracy.mp4
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2. Accuracy.srt
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2. Combining Over and Under-sampling - Demo.mp4
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2. Course Curriculum Overview.mp4
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2. Foundations of Ensemble Learning.mp4
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2. Nature of the imbalanced class.mp4
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2. Nature of the imbalanced class.srt
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2. Probability Calibration Curves.mp4
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2. Probability Calibration Curves.srt
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2. Random Over-Sampling.mp4
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2. Random Over-Sampling.srt
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2. Random Under-Sampling - Intro.mp4
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25.6 MB
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2. Random Under-Sampling - Intro.srt
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2. Types of Cost.mp4
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44 MB
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2. Types of Cost.srt
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20. Instance Hardness Threshold - Intro.mp4
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20. Instance Hardness Threshold - Intro.srt
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21. Instance Hardness Threshold - Demo.mp4
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21. Instance Hardness Threshold - Demo.srt
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22. Undersampling Method Comparison.mp4
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22. Undersampling Method Comparison.srt
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23. Summary Table.html
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102.4 B
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23.1 Undersampling-Comparison.pdf
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205.5 KB
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3. Accuracy - Demo.mp4
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47.6 MB
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3. Accuracy - Demo.srt
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3. Approaches to work with imbalanced datasets - Overview.mp4
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20.2 MB
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3. Approaches to work with imbalanced datasets - Overview.srt
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3. Bagging.mp4
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3. Bagging.srt
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3. Comparison of Over and Under-sampling Methods.mp4
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36.5 MB
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3. Comparison of Over and Under-sampling Methods.srt
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3. Course Material.mp4
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11 MB
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3. Course Material.srt
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3. Obtaining the Cost.mp4
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19 MB
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3. Obtaining the Cost.srt
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3. Probability Calibration Curves - Demo.mp4
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3. Probability Calibration Curves - Demo.srt
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3. Random Over-Sampling - Demo.mp4
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35.2 MB
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3. Random Over-Sampling - Demo.srt
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3. Random Under-Sampling - Demo.mp4
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66.9 MB
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3. Random Under-Sampling - Demo.srt
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13.5 KB
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4. Additional Reading Resources (Optional).html
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1 KB
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4. Bagging plus Over- or Under-Sampling.mp4
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42.9 MB
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4. Bagging plus Over- or Under-Sampling.srt
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6.4 KB
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4. Brier Score.mp4
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17.1 MB
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4. Brier Score.srt
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3.7 KB
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4. Code Jupyter notebooks.html
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921.6 B
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4. Condensed Nearest Neighbours - Intro.mp4
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32.4 MB
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4. Condensed Nearest Neighbours - Intro.srt
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4. Cost Sensitive Approaches.mp4
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4. Cost Sensitive Approaches.srt
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4. Precision, Recall and F-measure.mp4
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67 MB
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4. Precision, Recall and F-measure.srt
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15.1 KB
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4. SMOTE.mp4
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44.6 MB
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4. SMOTE.srt
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10 KB
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5. Boosting.mp4
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5. Boosting.srt
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5. Brier Score - Demo.mp4
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49 MB
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5. Brier Score - Demo.srt
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5. Condensed Nearest Neighbours - Demo.mp4
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5. Condensed Nearest Neighbours - Demo.srt
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5. Install Yellowbrick.html
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716.8 B
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5. Misclassification Cost in Logistic Regression.mp4
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18.7 MB
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5. Misclassification Cost in Logistic Regression.srt
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3.6 KB
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5. Presentations covered in the course.html
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307.2 B
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5. SMOTE - Demo.mp4
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18.4 MB
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5. SMOTE - Demo.srt
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3.2 KB
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6. Boosting plus Re-Sampling.mp4
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47.3 MB
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6. Boosting plus Re-Sampling.srt
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8 KB
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6. Misclassification Cost in Decision Trees.mp4
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6. Misclassification Cost in Decision Trees.srt
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6. Precision, Recall and F-measure - Demo.mp4
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80.3 MB
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6. Precision, Recall and F-measure - Demo.srt
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12.2 KB
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6. Python package Imbalanced-learn.html
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716.8 B
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6. SMOTE-NC.mp4
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48 MB
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6. SMOTE-NC.srt
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6. Tomek Links - Intro.mp4
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19 MB
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6. Tomek Links - Intro.srt
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6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4
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29.6 MB
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6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt
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7. Calibrating a Classifier.mp4
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27.2 MB
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7. Calibrating a Classifier.srt
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5.9 KB
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7. Confusion tables, FPR and FNR.mp4
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29.7 MB
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7. Confusion tables, FPR and FNR.srt
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7. Cost Sensitive Learning with Scikit-learn- Demo.mp4
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56.1 MB
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7. Cost Sensitive Learning with Scikit-learn- Demo.srt
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9 KB
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7. Download Datasets.html
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307.2 B
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7. Hybdrid Methods.mp4
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30.5 MB
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7. Hybdrid Methods.srt
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5.3 KB
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7. SMOTE-NC - Demo.mp4
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21.4 MB
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7. SMOTE-NC - Demo.srt
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3.3 KB
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7. Tomek Links - Demo.mp4
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24 MB
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7. Tomek Links - Demo.srt
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4.1 KB
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8. ADASYN.mp4
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31.6 MB
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8. ADASYN.srt
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7.7 KB
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8. Additional resources for Machine Learning and Python programming.html
|
HTML
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2.6 KB
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8. Calibrating a Classifier - Demo.mp4
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46.7 MB
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8. Calibrating a Classifier - Demo.srt
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7.3 KB
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8. Confusion tables, FPR and FNR - Demo.mp4
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49.1 MB
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8. Confusion tables, FPR and FNR - Demo.srt
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9.6 KB
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8. Ensemble Methods - Demo.mp4
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70.8 MB
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8. Ensemble Methods - Demo.srt
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11.8 KB
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8. Find Optimal Cost with hyperparameter tuning.mp4
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22.9 MB
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8. Find Optimal Cost with hyperparameter tuning.srt
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4.4 KB
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8. One Sided Selection - Intro.mp4
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11.9 MB
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8. One Sided Selection - Intro.srt
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2.8 KB
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9. ADASYN - Demo.mp4
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20.9 MB
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9. ADASYN - Demo.srt
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3.7 KB
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9. Additional Reading Resources.html
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2 KB
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9. Bayes Conditional Risk.mp4
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72 MB
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9. Bayes Conditional Risk.srt
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14.7 KB
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9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4
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52 MB
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9. Calibrating a Classfiier after SMOTE or Under-sampling.srt
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10.4 KB
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9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4
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MP4
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23.1 MB
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9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt
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5.2 KB
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9. One Sided Selection - Demo.mp4
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25.6 MB
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9. One Sided Selection - Demo.srt
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4.7 KB
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[CourseClub.ME].url
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URL
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102.4 B
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[FCS Forum].url
|
URL
|
102.4 B
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[FreeCourseSite.com].url
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URL
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102.4 B
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![Udemy - Machine Learning with Imbalanced Data [FCS]](/static/img/no-torrent-image.png "Udemy - Machine Learning with Imbalanced Data [FCS]")
