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

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

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种子哈希：dacde09eac2d17b68a03836303daeee0b6bf4bc6
文件大小： 2.95G
已经下载：1125次
下载速度：极快
收录时间：2021-05-21
最近下载：2025-09-08

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文件列表

  • 3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4 91.0 MB
  • 3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.mp4 84.2 MB
  • 8. Cost Sensitive Learning/9. Bayes Conditional Risk.mp4 75.5 MB
  • 7. Ensemble Methods/8. Ensemble Methods - Demo.mp4 74.3 MB
  • 7. Ensemble Methods/5. Boosting.mp4 74.0 MB
  • 3. Evaluation Metrics/4. Precision, Recall and F-measure.mp4 70.2 MB
  • 4. Udersampling/3. Random Under-Sampling - Demo.mp4 70.2 MB
  • 9. Probability Calibration/3. Probability Calibration Curves - Demo.mp4 68.0 MB
  • 8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.mp4 58.8 MB
  • 4. Udersampling/5. Condensed Nearest Neighbours - Demo.mp4 55.3 MB
  • 9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4 54.5 MB
  • 3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.mp4 51.5 MB
  • 9. Probability Calibration/5. Brier Score - Demo.mp4 51.4 MB
  • 5. Oversampling/6. SMOTE-NC.mp4 50.4 MB
  • 3. Evaluation Metrics/3. Accuracy - Demo.mp4 49.9 MB
  • 4. Udersampling/22. Undersampling Method Comparison.mp4 49.8 MB
  • 7. Ensemble Methods/6. Boosting plus Re-Sampling.mp4 49.6 MB
  • 9. Probability Calibration/8. Calibrating a Classifier - Demo.mp4 49.0 MB
  • 5. Oversampling/10. Borderline SMOTE.mp4 48.4 MB
  • 5. Oversampling/4. SMOTE.mp4 46.8 MB
  • 8. Cost Sensitive Learning/2. Types of Cost.mp4 46.1 MB
  • 7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.mp4 45.0 MB
  • 8. Cost Sensitive Learning/10. MetaCost.mp4 44.6 MB
  • 3. Evaluation Metrics/13. Precision-Recall Curve.mp4 42.5 MB
  • 5. Oversampling/16. Over-Sampling Method Comparison.mp4 41.7 MB
  • 3. Evaluation Metrics/11. ROC-AUC.mp4 41.2 MB
  • 5. Oversampling/13. SVM SMOTE - Demo.mp4 38.8 MB
  • 8. Cost Sensitive Learning/12. Optional MetaCost Base Code.mp4 38.7 MB
  • 6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.mp4 38.7 MB
  • 6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.mp4 38.3 MB
  • 5. Oversampling/3. Random Over-Sampling - Demo.mp4 36.9 MB
  • 2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.mp4 36.8 MB
  • 6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.mp4 36.0 MB
  • 9. Probability Calibration/1. Probability Calibration.mp4 35.7 MB
  • 2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.mp4 34.9 MB
  • 8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.mp4 34.3 MB
  • 4. Udersampling/4. Condensed Nearest Neighbours - Intro.mp4 34.0 MB
  • 1. Introduction/1. Introduction.mp4 33.8 MB
  • 5. Oversampling/8. ADASYN.mp4 33.1 MB
  • 3. Evaluation Metrics/12. ROC-AUC - Demo.mp4 33.1 MB
  • 4. Udersampling/1. Under-Sampling Methods - Introduction.mp4 33.0 MB
  • 4. Udersampling/11. Edited Nearest Neighbours - Demo.mp4 32.3 MB
  • 4. Udersampling/21. Instance Hardness Threshold - Demo.mp4 32.0 MB
  • 7. Ensemble Methods/7. Hybdrid Methods.mp4 32.0 MB
  • 3. Evaluation Metrics/7. Confusion tables, FPR and FNR.mp4 31.2 MB
  • 9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4 31.0 MB
  • 9. Probability Calibration/2. Probability Calibration Curves.mp4 30.2 MB
  • 5. Oversampling/14. K-Means SMOTE.mp4 28.9 MB
  • 9. Probability Calibration/7. Calibrating a Classifier.mp4 28.5 MB
  • 7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.mp4 27.8 MB
  • 4. Udersampling/19. NearMiss - Demo.mp4 27.6 MB
  • 4. Udersampling/2. Random Under-Sampling - Intro.mp4 26.9 MB
  • 4. Udersampling/9. One Sided Selection - Demo.mp4 26.8 MB
  • 5. Oversampling/12. SVM SMOTE.mp4 26.5 MB
  • 9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.mp4 26.4 MB
  • 5. Oversampling/15. K-Means SMOTE - Demo.mp4 26.0 MB
  • 5. Oversampling/11. Borderline SMOTE - Demo.mp4 26.0 MB
  • 4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.mp4 25.5 MB
  • 4. Udersampling/7. Tomek Links - Demo.mp4 25.1 MB
  • 3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4 24.2 MB
  • 4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.mp4 24.2 MB
  • 8. Cost Sensitive Learning/11. MetaCost - Demo.mp4 24.1 MB
  • 8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.mp4 24.0 MB
  • 4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.mp4 24.0 MB
  • 4. Udersampling/15. All KNN - Demo.mp4 23.8 MB
  • 4. Udersampling/10. Edited Nearest Neighbours - Intro.mp4 23.7 MB
  • 3. Evaluation Metrics/2. Accuracy.mp4 22.5 MB
  • 5. Oversampling/7. SMOTE-NC - Demo.mp4 22.5 MB
  • 8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.mp4 22.3 MB
  • 5. Oversampling/1. Over-Sampling Methods - Introduction.mp4 22.1 MB
  • 5. Oversampling/9. ADASYN - Demo.mp4 22.0 MB
  • 3. Evaluation Metrics/16. Probability.mp4 21.6 MB
  • 2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.mp4 21.2 MB
  • 7. Ensemble Methods/2. Foundations of Ensemble Learning.mp4 20.7 MB
  • 4. Udersampling/20. Instance Hardness Threshold - Intro.mp4 20.7 MB
  • 4. Udersampling/6. Tomek Links - Intro.mp4 19.9 MB
  • 8. Cost Sensitive Learning/3. Obtaining the Cost.mp4 19.9 MB
  • 8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.mp4 19.6 MB
  • 5. Oversampling/5. SMOTE - Demo.mp4 19.3 MB
  • 7. Ensemble Methods/3. Bagging.mp4 19.1 MB
  • 3. Evaluation Metrics/14. Precision-Recall Curve - Demo.mp4 19.0 MB
  • 1. Introduction/2. Course Curriculum Overview.mp4 18.4 MB
  • 4. Udersampling/18. NearMiss - Intro.mp4 18.0 MB
  • 9. Probability Calibration/4. Brier Score.mp4 18.0 MB
  • 4. Udersampling/14. All KNN - Intro.mp4 17.1 MB
  • 4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.mp4 16.7 MB
  • 5. Oversampling/2. Random Over-Sampling.mp4 16.4 MB
  • 4. Udersampling/8. One Sided Selection - Intro.mp4 12.5 MB
  • 1. Introduction/3. Course Material.mp4 11.5 MB
  • 3. Evaluation Metrics/1. Introduction to Performance Metrics.mp4 11.3 MB
  • 8. Cost Sensitive Learning/4. Cost Sensitive Approaches.mp4 10.8 MB
  • 4. Udersampling/23.1 Undersampling-Comparison.pdf 210.5 kB
  • 3. Evaluation Metrics/4. Precision, Recall and F-measure.srt 15.5 kB
  • 8. Cost Sensitive Learning/9. Bayes Conditional Risk.srt 15.0 kB
  • 4. Udersampling/3. Random Under-Sampling - Demo.srt 13.8 kB
  • 3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt 12.5 kB
  • 3. Evaluation Metrics/6. Precision, Recall and F-measure - Demo.srt 12.5 kB
  • 8. Cost Sensitive Learning/2. Types of Cost.srt 12.3 kB
  • 7. Ensemble Methods/8. Ensemble Methods - Demo.srt 12.1 kB
  • 9. Probability Calibration/3. Probability Calibration Curves - Demo.srt 11.8 kB
  • 7. Ensemble Methods/5. Boosting.srt 10.9 kB
  • 5. Oversampling/6. SMOTE-NC.srt 10.6 kB
  • 9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.srt 10.6 kB
  • 5. Oversampling/4. SMOTE.srt 10.3 kB
  • 3. Evaluation Metrics/8. Confusion tables, FPR and FNR - Demo.srt 9.8 kB
  • 5. Oversampling/10. Borderline SMOTE.srt 9.5 kB
  • 4. Udersampling/22. Undersampling Method Comparison.srt 9.5 kB
  • 3. Evaluation Metrics/13. Precision-Recall Curve.srt 9.5 kB
  • 4. Udersampling/5. Condensed Nearest Neighbours - Demo.srt 9.4 kB
  • 8. Cost Sensitive Learning/7. Cost Sensitive Learning with Scikit-learn- Demo.srt 9.2 kB
  • 9. Probability Calibration/5. Brier Score - Demo.srt 9.0 kB
  • 8. Cost Sensitive Learning/10. MetaCost.srt 8.7 kB
  • 3. Evaluation Metrics/11. ROC-AUC.srt 8.5 kB
  • 4. Udersampling/4. Condensed Nearest Neighbours - Intro.srt 8.5 kB
  • 7. Ensemble Methods/6. Boosting plus Re-Sampling.srt 8.2 kB
  • 8. Cost Sensitive Learning/1. Cost-sensitive Learning - Intro.srt 8.0 kB
  • 5. Oversampling/8. ADASYN.srt 7.9 kB
  • 8. Cost Sensitive Learning/12. Optional MetaCost Base Code.srt 7.6 kB
  • 3. Evaluation Metrics/7. Confusion tables, FPR and FNR.srt 7.5 kB
  • 9. Probability Calibration/1. Probability Calibration.srt 7.5 kB
  • 3. Evaluation Metrics/3. Accuracy - Demo.srt 7.5 kB
  • 9. Probability Calibration/8. Calibrating a Classifier - Demo.srt 7.4 kB
  • 6. Over and Undersampling/1. Combining Over and Under-sampling - Intro.srt 7.4 kB
  • 5. Oversampling/16. Over-Sampling Method Comparison.srt 7.3 kB
  • 9. Probability Calibration/2. Probability Calibration Curves.srt 6.8 kB
  • 4. Udersampling/2. Random Under-Sampling - Intro.srt 6.8 kB
  • 4. Udersampling/1. Under-Sampling Methods - Introduction.srt 6.7 kB
  • 6. Over and Undersampling/3. Comparison of Over and Under-sampling Methods.srt 6.7 kB
  • 2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.srt 6.6 kB
  • 7. Ensemble Methods/4. Bagging plus Over- or Under-Sampling.srt 6.5 kB
  • 5. Oversampling/3. Random Over-Sampling - Demo.srt 6.5 kB
  • 6. Over and Undersampling/2. Combining Over and Under-sampling - Demo.srt 6.4 kB
  • 9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt 6.4 kB
  • 5. Oversampling/12. SVM SMOTE.srt 6.2 kB
  • 5. Oversampling/14. K-Means SMOTE.srt 6.2 kB
  • 2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.srt 6.1 kB
  • 9. Probability Calibration/7. Calibrating a Classifier.srt 6.0 kB
  • 3. Evaluation Metrics/16. Probability.srt 5.7 kB
  • 4. Udersampling/12. Repeated Edited Nearest Neighbours - Intro.srt 5.5 kB
  • 7. Ensemble Methods/1. Ensemble methods with Imbalanced Data.srt 5.5 kB
  • 4. Udersampling/10. Edited Nearest Neighbours - Intro.srt 5.5 kB
  • 3. Evaluation Metrics/12. ROC-AUC - Demo.srt 5.5 kB
  • 3. Evaluation Metrics/2. Accuracy.srt 5.4 kB
  • 7. Ensemble Methods/7. Hybdrid Methods.srt 5.4 kB
  • 4. Udersampling/6. Tomek Links - Intro.srt 5.4 kB
  • 3. Evaluation Metrics/9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt 5.4 kB
  • 4. Udersampling/11. Edited Nearest Neighbours - Demo.srt 5.3 kB
  • 4. Udersampling/16. Neighbourhood Cleaning Rule - Intro.srt 5.1 kB
  • 4. Udersampling/20. Instance Hardness Threshold - Intro.srt 5.1 kB
  • 5. Oversampling/13. SVM SMOTE - Demo.srt 5.0 kB
  • 4. Udersampling/21. Instance Hardness Threshold - Demo.srt 5.0 kB
  • 2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.srt 4.8 kB
  • 4. Udersampling/9. One Sided Selection - Demo.srt 4.8 kB
  • 9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.srt 4.7 kB
  • 8. Cost Sensitive Learning/3. Obtaining the Cost.srt 4.7 kB
  • 4. Udersampling/19. NearMiss - Demo.srt 4.7 kB
  • 8. Cost Sensitive Learning/11. MetaCost - Demo.srt 4.6 kB
  • 4. Udersampling/18. NearMiss - Intro.srt 4.5 kB
  • 8. Cost Sensitive Learning/8. Find Optimal Cost with hyperparameter tuning.srt 4.5 kB
  • 5. Oversampling/1. Over-Sampling Methods - Introduction.srt 4.5 kB
  • 4. Udersampling/14. All KNN - Intro.srt 4.4 kB
  • 8. Cost Sensitive Learning/6. Misclassification Cost in Decision Trees.srt 4.2 kB
  • 4. Udersampling/7. Tomek Links - Demo.srt 4.2 kB
  • 1. Introduction/1. Introduction.srt 4.1 kB
  • 4. Udersampling/13. Repeated Edited Nearest Neighbours - Demo.srt 4.0 kB
  • 5. Oversampling/15. K-Means SMOTE - Demo.srt 4.0 kB
  • 1. Introduction/2. Course Curriculum Overview.srt 4.0 kB
  • 5. Oversampling/9. ADASYN - Demo.srt 3.8 kB
  • 5. Oversampling/2. Random Over-Sampling.srt 3.8 kB
  • 9. Probability Calibration/4. Brier Score.srt 3.8 kB
  • 8. Cost Sensitive Learning/5. Misclassification Cost in Logistic Regression.srt 3.7 kB
  • 5. Oversampling/11. Borderline SMOTE - Demo.srt 3.7 kB
  • 4. Udersampling/15. All KNN - Demo.srt 3.6 kB
  • 3. Evaluation Metrics/14. Precision-Recall Curve - Demo.srt 3.5 kB
  • 5. Oversampling/7. SMOTE-NC - Demo.srt 3.4 kB
  • 3. Evaluation Metrics/1. Introduction to Performance Metrics.srt 3.4 kB
  • 7. Ensemble Methods/3. Bagging.srt 3.3 kB
  • 7. Ensemble Methods/2. Foundations of Ensemble Learning.srt 3.3 kB
  • 5. Oversampling/5. SMOTE - Demo.srt 3.2 kB
  • 4. Udersampling/8. One Sided Selection - Intro.srt 2.9 kB
  • 4. Udersampling/17. Neighbourhood Cleaning Rule - Demo.srt 2.7 kB
  • 1. Introduction/8. Additional resources for Machine Learning and Python programming.html 2.7 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 1.9 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 1.1 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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