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[FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data
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2021-8-10 11:41
2024-9-11 14:02
112
2.94 GB
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Udemy
-
Machine
Learning
with
Imbalanced
Data
文件列表
1. Introduction/1. Introduction.mp4
32.25MB
1. Introduction/2. Course Curriculum Overview.mp4
17.54MB
1. Introduction/3. Course Material.mp4
10.96MB
2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.mp4
33.3MB
2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.mp4
35.11MB
2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.mp4
20.24MB
3. Evaluation Metrics/1. Introduction to Performance Metrics.mp4
10.79MB
3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4
86.77MB
3. Evaluation Metrics/11. ROC-AUC.mp4
39.25MB
3. Evaluation Metrics/12. ROC-AUC - Demo.mp4
31.56MB
3. Evaluation Metrics/13. Precision-Recall Curve.mp4
40.5MB
3. Evaluation Metrics/14. Precision-Recall Curve - Demo.mp4
18.08MB
3. Evaluation Metrics/16. Probability.mp4
20.64MB
3. Evaluation Metrics/2. Accuracy.mp4
21.44MB
3. Evaluation Metrics/3. Accuracy - Demo.mp4
47.61MB
3. Evaluation Metrics/4. Precision, Recall and F-measure.mp4
66.98MB
3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.mp4
80.33MB
3. Evaluation Metrics/7. Confusion tables, FPR and FNR.mp4
29.72MB
3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.mp4
49.08MB
3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4
23.06MB
4. Udersampling/1. Under-Sampling Methods - Introduction.mp4
31.45MB
4. Udersampling/10. Edited Nearest Neighbours - Intro.mp4
22.57MB
4. Udersampling/11. Edited Nearest Neighbours - Demo.mp4
30.82MB
4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.mp4
24.27MB
4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.mp4
22.89MB
4. Udersampling/14. All KNN - Intro.mp4
16.27MB
4. Udersampling/15. All KNN - Demo.mp4
22.65MB
4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.mp4
23.04MB
4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.mp4
15.9MB
4. Udersampling/18. NearMiss - Intro.mp4
17.18MB
4. Udersampling/19. NearMiss - Demo.mp4
26.33MB
4. Udersampling/2. Random Under-Sampling - Intro.mp4
25.62MB
4. Udersampling/20. Instance Hardness Threshold - Intro.mp4
19.7MB
4. Udersampling/21. Instance Hardness Threshold - Demo.mp4
30.54MB
4. Udersampling/22. Undersampling Method Comparison.mp4
47.52MB
4. Udersampling/3. Random Under-Sampling - Demo.mp4
66.91MB
4. Udersampling/4. Condensed Nearest Neighbours - Intro.mp4
32.43MB
4. Udersampling/5. Condensed Nearest Neighbours - Demo.mp4
52.71MB
4. Udersampling/6. Tomek Links - Intro.mp4
18.97MB
4. Udersampling/7. Tomek Links - Demo.mp4
23.98MB
4. Udersampling/8. One Sided Selection - Intro.mp4
11.9MB
4. Udersampling/9. One Sided Selection - Demo.mp4
25.59MB
5. Oversampling/1. Over-Sampling Methods - Introduction.mp4
21.09MB
5. Oversampling/10. Borderline SMOTE.mp4
46.2MB
5. Oversampling/11. Borderline SMOTE - Demo.mp4
24.77MB
5. Oversampling/12. SVM SMOTE.mp4
25.27MB
5. Oversampling/13. SVM SMOTE - Demo.mp4
37.01MB
5. Oversampling/14. K-Means SMOTE.mp4
27.6MB
5. Oversampling/15. K-Means SMOTE - Demo.mp4
24.77MB
5. Oversampling/16. Over-Sampling Method Comparison.mp4
39.77MB
5. Oversampling/2. Random Over-Sampling.mp4
15.65MB
5. Oversampling/3. Random Over-Sampling - Demo.mp4
35.2MB
5. Oversampling/4. SMOTE.mp4
44.61MB
5. Oversampling/5. SMOTE - Demo.mp4
18.38MB
5. Oversampling/6. SMOTE-NC.mp4
48.03MB
5. Oversampling/7. SMOTE-NC - Demo.mp4
21.43MB
5. Oversampling/8. ADASYN.mp4
31.6MB
5. Oversampling/9. ADASYN - Demo.mp4
20.95MB
6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.mp4
36.9MB
6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.mp4
34.33MB
6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.mp4
36.54MB
7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.mp4
26.54MB
7. Ensemble Methods/2. Foundations of Ensemble Learning.mp4
19.71MB
7. Ensemble Methods/3. Bagging.mp4
18.19MB
7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.mp4
42.87MB
7. Ensemble Methods/5. Boosting.mp4
70.58MB
7. Ensemble Methods/6. Boosting plus Re-Sampling.mp4
47.31MB
7. Ensemble Methods/7. Hybdrid Methods.mp4
30.49MB
7. Ensemble Methods/8. Ensemble Methods - Demo.mp4
70.85MB
8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.mp4
32.73MB
8. Cost Sensitive Learning/10. MetaCost.mp4
42.57MB
8. Cost Sensitive Learning/11. MetaCost - Demo.mp4
22.94MB
8. Cost Sensitive Learning/12. Optional MetaCost Base Code.mp4
36.92MB
8. Cost Sensitive Learning/2. Types of Cost.mp4
43.99MB
8. Cost Sensitive Learning/3. Obtaining the Cost.mp4
18.96MB
8. Cost Sensitive Learning/4. Cost Sensitive Approaches.mp4
10.33MB
8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.mp4
18.69MB
8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.mp4
21.26MB
8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.mp4
56.06MB
8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.mp4
22.9MB
8. Cost Sensitive Learning/9. Bayes Conditional Risk.mp4
72.04MB
9. Probability Calibration/1. Probability Calibration.mp4
34.09MB
9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.mp4
25.19MB
9. Probability Calibration/2. Probability Calibration Curves.mp4
28.76MB
9. Probability Calibration/3. Probability Calibration Curves - Demo.mp4
64.88MB
9. Probability Calibration/4. Brier Score.mp4
17.15MB
9. Probability Calibration/5. Brier Score - Demo.mp4
49.02MB
9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4
29.58MB
9. Probability Calibration/7. Calibrating a Classifier.mp4
27.19MB
9. Probability Calibration/8. Calibrating a Classifier - Demo.mp4
46.73MB
9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4
52MB
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