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

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

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收录时间：2024-01-25
最近下载：2025-09-20

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文件列表

  • 04 - Udersampling/021 Instance Hardness Threshold - Demo.mp4 107.6 MB
  • 05 - Oversampling/016 SVM SMOTE.mp4 86.3 MB
  • 03 - Evaluation Metrics/006 Precision, Recall and F-measure - Demo.mp4 79.8 MB
  • 03 - Evaluation Metrics/012 Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4 77.9 MB
  • 04 - Udersampling/025 Setting up a classifier with under-sampling and cross-validation.mp4 67.0 MB
  • 09 - Probability Calibration/003 Probability Calibration Curves - Demo.mp4 64.0 MB
  • 04 - Udersampling/003 Random Under-Sampling - Demo.mp4 61.5 MB
  • 03 - Evaluation Metrics/023 PR Curves in Multiclass - Demo.mp4 57.4 MB
  • 08 - Cost Sensitive Learning/007 Cost Sensitive Learning with Scikit-learn.mp4 56.2 MB
  • 03 - Evaluation Metrics/021 Metrics for Multiclass - Demo.mp4 54.5 MB
  • 04 - Udersampling/005 Condensed Nearest Neighbours - Demo.mp4 52.5 MB
  • 04 - Udersampling/022 Instance Hardness Threshold Multiclass Demo.mp4 50.6 MB
  • 03 - Evaluation Metrics/024 ROC Curve in Multiclass - Demo.mp4 48.3 MB
  • 03 - Evaluation Metrics/008 Confusion tables, FPR and FNR - Demo.mp4 48.2 MB
  • 09 - Probability Calibration/009 Calibrating a Classfiier after SMOTE or Under-sampling.mp4 48.0 MB
  • 05 - Oversampling/010 SMOTE-N.mp4 47.6 MB
  • 05 - Oversampling/011 SMOTE-N Demo.mp4 47.0 MB
  • 05 - Oversampling/006 SMOTE.mp4 46.8 MB
  • 09 - Probability Calibration/008 Calibrating a Classifier - Demo.mp4 46.6 MB
  • 09 - Probability Calibration/005 Brier Score - Demo.mp4 44.9 MB
  • 08 - Cost Sensitive Learning/009 Bayes Conditional Risk.mp4 44.7 MB
  • 07 - Ensemble Methods/006 Boosting plus Re-Sampling.mp4 43.7 MB
  • 04 - Udersampling/023 Undersampling Method Comparison.mp4 43.1 MB
  • 03 - Evaluation Metrics/003 Accuracy - Demo.mp4 41.5 MB
  • 04 - Udersampling/004 Condensed Nearest Neighbours - Intro.mp4 39.4 MB
  • 07 - Ensemble Methods/004 Bagging plus Over- or Under-Sampling.mp4 38.8 MB
  • 04 - Udersampling/015 All KNN - Demo.mp4 37.9 MB
  • 05 - Oversampling/018 SVM SMOTE - Demo.mp4 37.4 MB
  • 08 - Cost Sensitive Learning/002 Types of Cost.mp4 37.1 MB
  • 05 - Oversampling/014 Borderline SMOTE.mp4 36.1 MB
  • 03 - Evaluation Metrics/013 ROC-AUC.mp4 36.0 MB
  • 08 - Cost Sensitive Learning/010 MetaCost.mp4 35.6 MB
  • 06 - Over and Undersampling/003 Comparison of Over and Under-sampling Methods.mp4 33.6 MB
  • 04 - Udersampling/001 Under-Sampling Methods - Introduction.mp4 33.1 MB
  • 07 - Ensemble Methods/008 Ensemble Methods - Demo.mp4 32.8 MB
  • 06 - Over and Undersampling/001 Combining Over and Under-sampling - Intro.mp4 32.0 MB
  • 08 - Cost Sensitive Learning/012 Optional MetaCost Base Code.mp4 31.9 MB
  • 03 - Evaluation Metrics/004 Precision, Recall and F-measure.mp4 31.7 MB
  • 05 - Oversampling/019 K-Means SMOTE.mp4 31.3 MB
  • 03 - Evaluation Metrics/014 ROC-AUC - Demo.mp4 31.1 MB
  • 05 - Oversampling/023 How to Correctly Set Up a Classifier with Over-sampling.mp4 29.4 MB
  • 05 - Oversampling/022 Wrapping up the section.mp4 28.6 MB
  • 05 - Oversampling/021 Over-Sampling Method Comparison.mp4 28.2 MB
  • 07 - Ensemble Methods/005 Boosting.mp4 28.1 MB
  • 06 - Over and Undersampling/002 Combining Over and Under-sampling - Demo.mp4 27.7 MB
  • 04 - Udersampling/011 Edited Nearest Neighbours - Demo.mp4 27.6 MB
  • 03 - Evaluation Metrics/020 Metrics for Mutliclass.mp4 27.5 MB
  • 05 - Oversampling/003 Random Over-Sampling - Demo.mp4 27.4 MB
  • 07 - Ensemble Methods/009 Wrapping up.mp4 27.4 MB
  • 02 - Machine Learning with Imbalanced Data Overview/001 Imbalanced classes - Introduction.mp4 26.7 MB
  • 05 - Oversampling/012 ADASYN.mp4 26.5 MB
  • 02 - Machine Learning with Imbalanced Data Overview/002 Nature of the imbalanced class.mp4 26.0 MB
  • 04 - Udersampling/010 Edited Nearest Neighbours - Intro.mp4 24.6 MB
  • 05 - Oversampling/005 ROS with smoothing - Demo.mp4 24.5 MB
  • 05 - Oversampling/004 ROS with smoothing - Intro.mp4 24.5 MB
  • 09 - Probability Calibration/010 Calibrating a Classifier with Cost-sensitive Learning.mp4 23.1 MB
  • 05 - Oversampling/002 Random Over-Sampling.mp4 22.4 MB
  • 09 - Probability Calibration/007 Calibrating a Classifier.mp4 22.2 MB
  • 04 - Udersampling/020 Instance Hardness Threshold - Intro.mp4 21.5 MB
  • 05 - Oversampling/008 SMOTE-NC.mp4 21.5 MB
  • 08 - Cost Sensitive Learning/008 Find Optimal Cost with hyperparameter tuning.mp4 21.2 MB
  • 04 - Udersampling/013 Repeated Edited Nearest Neighbours - Demo.mp4 20.7 MB
  • 04 - Udersampling/019 NearMiss - Demo.mp4 19.9 MB
  • 05 - Oversampling/020 K-Means SMOTE - Demo.mp4 19.5 MB
  • 05 - Oversampling/009 SMOTE-NC - Demo.mp4 19.2 MB
  • 03 - Evaluation Metrics/016 Precision-Recall Curve - Demo.mp4 19.0 MB
  • 08 - Cost Sensitive Learning/011 MetaCost - Demo.mp4 18.6 MB
  • 05 - Oversampling/015 Borderline SMOTE - Demo.mp4 18.4 MB
  • 01 - Introduction/001 Course Curriculum Overview.mp4 18.4 MB
  • 05 - Oversampling/007 SMOTE - Demo.mp4 18.3 MB
  • 03 - Evaluation Metrics/010 Balanced accuracy - Demo.mp4 17.4 MB
  • 04 - Udersampling/007 Tomek Links - Demo.mp4 16.9 MB
  • 04 - Udersampling/009 One Sided Selection - Demo.mp4 16.9 MB
  • 05 - Oversampling/024 Setting Up a Classifier - Demo.mp4 16.8 MB
  • 03 - Evaluation Metrics/015 Precision-Recall Curve.mp4 16.6 MB
  • 09 - Probability Calibration/006 Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4 16.6 MB
  • 05 - Oversampling/013 ADASYN - Demo.mp4 16.4 MB
  • 08 - Cost Sensitive Learning/001 Cost-sensitive Learning - Intro.mp4 16.2 MB
  • 03 - Evaluation Metrics/007 Confusion tables, FPR and FNR.mp4 16.1 MB
  • 09 - Probability Calibration/001 Probability Calibration.mp4 15.9 MB
  • 04 - Udersampling/016 Neighbourhood Cleaning Rule - Intro.mp4 15.0 MB
  • 04 - Udersampling/018 NearMiss - Intro.mp4 14.5 MB
  • 04 - Udersampling/014 All KNN - Intro.mp4 14.4 MB
  • 04 - Udersampling/012 Repeated Edited Nearest Neighbours - Intro.mp4 14.3 MB
  • 09 - Probability Calibration/002 Probability Calibration Curves.mp4 14.3 MB
  • 07 - Ensemble Methods/001 Ensemble methods with Imbalanced Data.mp4 14.1 MB
  • 04 - Udersampling/017 Neighbourhood Cleaning Rule - Demo.mp4 13.1 MB
  • 02 - Machine Learning with Imbalanced Data Overview/003 Approaches to work with imbalanced datasets - Overview.mp4 12.7 MB
  • 07 - Ensemble Methods/007 Hybdrid Methods.mp4 12.6 MB
  • 03 - Evaluation Metrics/011 Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4 12.5 MB
  • 04 - Udersampling/024 Wrapping up the section.mp4 12.3 MB
  • 03 - Evaluation Metrics/002 Accuracy.mp4 11.9 MB
  • 03 - Evaluation Metrics/022 PR and ROC Curves for Multiclass.mp4 11.9 MB
  • 04 - Udersampling/002 Random Under-Sampling - Intro.mp4 11.2 MB
  • 05 - Oversampling/001 Over-Sampling Methods - Introduction.mp4 11.2 MB
  • 03 - Evaluation Metrics/019 Probability.mp4 10.6 MB
  • 04 - Udersampling/008 One Sided Selection - Intro.mp4 10.4 MB
  • 08 - Cost Sensitive Learning/005 Misclassification Cost in Logistic Regression.mp4 10.2 MB
  • 04 - Udersampling/006 Tomek Links - Intro.mp4 10.2 MB
  • 08 - Cost Sensitive Learning/006 Misclassification Cost in Decision Trees.mp4 10.2 MB
  • 07 - Ensemble Methods/002 Foundations of Ensemble Learning.mp4 10.0 MB
  • 08 - Cost Sensitive Learning/003 Obtaining the Cost.mp4 9.7 MB
  • 01 - Introduction/002 Course Material.mp4 9.7 MB
  • 07 - Ensemble Methods/003 Bagging.mp4 9.3 MB
  • 03 - Evaluation Metrics/009 Balanced Accuracy.mp4 8.1 MB
  • 06 - Over and Undersampling/005 Wrapping up.mp4 7.9 MB
  • 09 - Probability Calibration/004 Brier Score.mp4 7.6 MB
  • 03 - Evaluation Metrics/001 Introduction to Performance Metrics.mp4 7.1 MB
  • 08 - Cost Sensitive Learning/004 Cost Sensitive Approaches.mp4 5.5 MB
  • 04 - Udersampling/026 undersampling-comparison.pdf 882.5 kB
  • 05 - Oversampling/025 oversampling-comparison.pdf 320.0 kB
  • 05 - Oversampling/010 SMOTE-N_en.srt 22.7 kB
  • 04 - Udersampling/021 Instance Hardness Threshold - Demo_en.srt 20.6 kB
  • 05 - Oversampling/016 SVM SMOTE_en.srt 19.9 kB
  • 05 - Oversampling/019 K-Means SMOTE_en.srt 16.9 kB
  • 03 - Evaluation Metrics/004 Precision, Recall and F-measure_en.srt 15.5 kB
  • 08 - Cost Sensitive Learning/009 Bayes Conditional Risk_en.srt 15.0 kB
  • 04 - Udersampling/003 Random Under-Sampling - Demo_en.srt 14.2 kB
  • 04 - Udersampling/025 Setting up a classifier with under-sampling and cross-validation_en.srt 13.8 kB
  • 03 - Evaluation Metrics/020 Metrics for Mutliclass_en.srt 13.0 kB
  • 05 - Oversampling/022 Wrapping up the section_en.srt 12.7 kB
  • 03 - Evaluation Metrics/006 Precision, Recall and F-measure - Demo_en.srt 12.5 kB
  • 08 - Cost Sensitive Learning/002 Types of Cost_en.srt 12.3 kB
  • 07 - Ensemble Methods/008 Ensemble Methods - Demo_en.srt 12.1 kB
  • 09 - Probability Calibration/003 Probability Calibration Curves - Demo_en.srt 11.8 kB
  • 03 - Evaluation Metrics/012 Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo_en.srt 11.4 kB
  • 07 - Ensemble Methods/005 Boosting_en.srt 10.9 kB
  • 04 - Udersampling/020 Instance Hardness Threshold - Intro_en.srt 10.7 kB
  • 05 - Oversampling/008 SMOTE-NC_en.srt 10.6 kB
  • 09 - Probability Calibration/009 Calibrating a Classfiier after SMOTE or Under-sampling_en.srt 10.6 kB
  • 03 - Evaluation Metrics/023 PR Curves in Multiclass - Demo_en.srt 10.4 kB
  • 03 - Evaluation Metrics/021 Metrics for Multiclass - Demo_en.srt 10.3 kB
  • 05 - Oversampling/006 SMOTE_en.srt 10.3 kB
  • 03 - Evaluation Metrics/008 Confusion tables, FPR and FNR - Demo_en.srt 9.8 kB
  • 04 - Udersampling/023 Undersampling Method Comparison_en.srt 9.5 kB
  • 04 - Udersampling/004 Condensed Nearest Neighbours - Intro_en.srt 9.5 kB
  • 05 - Oversampling/014 Borderline SMOTE_en.srt 9.4 kB
  • 04 - Udersampling/022 Instance Hardness Threshold Multiclass Demo_en.srt 9.4 kB
  • 04 - Udersampling/005 Condensed Nearest Neighbours - Demo_en.srt 9.4 kB
  • 08 - Cost Sensitive Learning/007 Cost Sensitive Learning with Scikit-learn_en.srt 9.2 kB
  • 09 - Probability Calibration/005 Brier Score - Demo_en.srt 9.0 kB
  • 05 - Oversampling/011 SMOTE-N Demo_en.srt 9.0 kB
  • 03 - Evaluation Metrics/024 ROC Curve in Multiclass - Demo_en.srt 8.9 kB
  • 08 - Cost Sensitive Learning/010 MetaCost_en.srt 8.7 kB
  • 03 - Evaluation Metrics/013 ROC-AUC_en.srt 8.6 kB
  • 03 - Evaluation Metrics/015 Precision-Recall Curve_en.srt 8.5 kB
  • 07 - Ensemble Methods/006 Boosting plus Re-Sampling_en.srt 8.2 kB
  • 05 - Oversampling/004 ROS with smoothing - Intro_en.srt 8.0 kB
  • 08 - Cost Sensitive Learning/001 Cost-sensitive Learning - Intro_en.srt 8.0 kB
  • 05 - Oversampling/012 ADASYN_en.srt 7.9 kB
  • 08 - Cost Sensitive Learning/012 Optional MetaCost Base Code_en.srt 7.6 kB
  • 04 - Udersampling/014 All KNN - Intro_en.srt 7.6 kB
  • 03 - Evaluation Metrics/007 Confusion tables, FPR and FNR_en.srt 7.5 kB
  • 04 - Udersampling/016 Neighbourhood Cleaning Rule - Intro_en.srt 7.5 kB
  • 09 - Probability Calibration/001 Probability Calibration_en.srt 7.5 kB
  • 09 - Probability Calibration/008 Calibrating a Classifier - Demo_en.srt 7.4 kB
  • 04 - Udersampling/015 All KNN - Demo_en.srt 7.4 kB
  • 05 - Oversampling/021 Over-Sampling Method Comparison_en.srt 7.1 kB
  • 06 - Over and Undersampling/001 Combining Over and Under-sampling - Intro_en.srt 7.0 kB
  • 05 - Oversampling/003 Random Over-Sampling - Demo_en.srt 6.9 kB
  • 09 - Probability Calibration/002 Probability Calibration Curves_en.srt 6.8 kB
  • 03 - Evaluation Metrics/003 Accuracy - Demo_en.srt 6.8 kB
  • 04 - Udersampling/001 Under-Sampling Methods - Introduction_en.srt 6.7 kB
  • 06 - Over and Undersampling/003 Comparison of Over and Under-sampling Methods_en.srt 6.7 kB
  • 07 - Ensemble Methods/009 Wrapping up_en.srt 6.7 kB
  • 02 - Machine Learning with Imbalanced Data Overview/001 Imbalanced classes - Introduction_en.srt 6.6 kB
  • 05 - Oversampling/023 How to Correctly Set Up a Classifier with Over-sampling_en.srt 6.6 kB
  • 04 - Udersampling/024 Wrapping up the section_en.srt 6.5 kB
  • 07 - Ensemble Methods/004 Bagging plus Over- or Under-Sampling_en.srt 6.5 kB
  • 06 - Over and Undersampling/002 Combining Over and Under-sampling - Demo_en.srt 6.5 kB
  • 09 - Probability Calibration/006 Under- and Over-sampling and Cost-sensitive learning on Probability Calibration_en.srt 6.4 kB
  • 05 - Oversampling/002 Random Over-Sampling_en.srt 6.3 kB
  • 03 - Evaluation Metrics/022 PR and ROC Curves for Multiclass_en.srt 6.1 kB
  • 02 - Machine Learning with Imbalanced Data Overview/002 Nature of the imbalanced class_en.srt 6.1 kB
  • 09 - Probability Calibration/007 Calibrating a Classifier_en.srt 6.0 kB
  • 04 - Udersampling/010 Edited Nearest Neighbours - Intro_en.srt 5.8 kB
  • 05 - Oversampling/005 ROS with smoothing - Demo_en.srt 5.7 kB
  • 03 - Evaluation Metrics/019 Probability_en.srt 5.7 kB
  • 04 - Udersampling/012 Repeated Edited Nearest Neighbours - Intro_en.srt 5.6 kB
  • 07 - Ensemble Methods/001 Ensemble methods with Imbalanced Data_en.srt 5.5 kB
  • 03 - Evaluation Metrics/014 ROC-AUC - Demo_en.srt 5.5 kB
  • 04 - Udersampling/008 One Sided Selection - Intro_en.srt 5.5 kB
  • 04 - Udersampling/006 Tomek Links - Intro_en.srt 5.4 kB
  • 07 - Ensemble Methods/007 Hybdrid Methods_en.srt 5.4 kB
  • 03 - Evaluation Metrics/002 Accuracy_en.srt 5.4 kB
  • 03 - Evaluation Metrics/011 Geometric Mean, Dominance, Index of Imbalanced Accuracy_en.srt 5.4 kB
  • 05 - Oversampling/024 Setting Up a Classifier - Demo_en.srt 5.4 kB
  • 04 - Udersampling/002 Random Under-Sampling - Intro_en.srt 5.3 kB
  • 04 - Udersampling/011 Edited Nearest Neighbours - Demo_en.srt 5.3 kB
  • 05 - Oversampling/018 SVM SMOTE - Demo_en.srt 5.0 kB
  • 02 - Machine Learning with Imbalanced Data Overview/003 Approaches to work with imbalanced datasets - Overview_en.srt 4.8 kB
  • 08 - Cost Sensitive Learning/003 Obtaining the Cost_en.srt 4.7 kB
  • 04 - Udersampling/019 NearMiss - Demo_en.srt 4.7 kB
  • 08 - Cost Sensitive Learning/011 MetaCost - Demo_en.srt 4.6 kB
  • 04 - Udersampling/018 NearMiss - Intro_en.srt 4.5 kB
  • 08 - Cost Sensitive Learning/008 Find Optimal Cost with hyperparameter tuning_en.srt 4.5 kB
  • 05 - Oversampling/001 Over-Sampling Methods - Introduction_en.srt 4.5 kB
  • 03 - Evaluation Metrics/001 Introduction to Performance Metrics_en.srt 4.3 kB
  • 09 - Probability Calibration/010 Calibrating a Classifier with Cost-sensitive Learning_en.srt 4.3 kB
  • 08 - Cost Sensitive Learning/006 Misclassification Cost in Decision Trees_en.srt 4.2 kB
  • 03 - Evaluation Metrics/009 Balanced Accuracy_en.srt 4.2 kB
  • 09 - Probability Calibration/010 Calibrating a Classifier with Cost-sensitive Learning_en.vtt 4.1 kB
  • 01 - Introduction/001 Course Curriculum Overview_en.srt 4.0 kB
  • 04 - Udersampling/013 Repeated Edited Nearest Neighbours - Demo_en.srt 4.0 kB
  • 05 - Oversampling/020 K-Means SMOTE - Demo_en.srt 4.0 kB
  • 04 - Udersampling/009 One Sided Selection - Demo_en.srt 3.9 kB
  • 04 - Udersampling/007 Tomek Links - Demo_en.srt 3.9 kB
  • 05 - Oversampling/013 ADASYN - Demo_en.srt 3.8 kB
  • 09 - Probability Calibration/004 Brier Score_en.srt 3.8 kB
  • 08 - Cost Sensitive Learning/005 Misclassification Cost in Logistic Regression_en.srt 3.7 kB
  • 05 - Oversampling/015 Borderline SMOTE - Demo_en.srt 3.7 kB
  • 03 - Evaluation Metrics/016 Precision-Recall Curve - Demo_en.srt 3.6 kB
  • 03 - Evaluation Metrics/010 Balanced accuracy - Demo_en.srt 3.4 kB
  • 05 - Oversampling/009 SMOTE-NC - Demo_en.srt 3.4 kB
  • 07 - Ensemble Methods/003 Bagging_en.srt 3.3 kB
  • 07 - Ensemble Methods/002 Foundations of Ensemble Learning_en.srt 3.3 kB
  • 05 - Oversampling/007 SMOTE - Demo_en.srt 3.2 kB
  • 01 - Introduction/007 Additional resources for Machine Learning and Python programming.html 3.0 kB
  • 04 - Udersampling/017 Neighbourhood Cleaning Rule - Demo_en.srt 2.7 kB
  • 06 - Over and Undersampling/005 Wrapping up_en.srt 2.6 kB
  • 07 - Ensemble Methods/010 Additional Reading Resources.html 2.0 kB
  • 08 - Cost Sensitive Learning/013 Additional Reading Resources.html 2.0 kB
  • 01 - Introduction/002 Course Material_en.srt 2.0 kB
  • 08 - Cost Sensitive Learning/004 Cost Sensitive Approaches_en.srt 1.9 kB
  • 03 - Evaluation Metrics/018 Additional reading resources (Optional).html 1.6 kB
  • 04 - Udersampling/026 Summary Table.html 1.1 kB
  • 02 - Machine Learning with Imbalanced Data Overview/004 Additional Reading Resources (Optional).html 1.1 kB
  • 01 - Introduction/003 Code Jupyter notebooks.html 961 Bytes
  • 11 - Next steps/001 Vote for the next course!.html 947 Bytes
  • 09 - Probability Calibration/011 Probability Additional reading resources.html 931 Bytes
  • 10 - Putting it all together/001 Examples.html 747 Bytes
  • 01 - Introduction/005 Python package Imbalanced-learn.html 716 Bytes
  • 03 - Evaluation Metrics/005 Install Yellowbrick.html 680 Bytes
  • 05 - Oversampling/017 Resources on SVMs.html 649 Bytes
  • 11 - Next steps/003 Bonus Lecture.html 625 Bytes
  • 11 - Next steps/002 Congratulations.html 578 Bytes
  • 06 - Over and Undersampling/004 Combine over and under-sampling manually.html 376 Bytes
  • 01 - Introduction/006 Download Datasets.html 354 Bytes
  • 05 - Oversampling/025 Summary Table.html 340 Bytes
  • 03 - Evaluation Metrics/017 Comparison of ROC and PR curves - Optional.html 321 Bytes
  • 01 - Introduction/004 Presentations covered in the course.html 286 Bytes
  • 03 - Evaluation Metrics/external-assets-links.txt 150 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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