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[FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data
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2022-11-9 08:42
2024-12-25 01:33
152
2.9 GB
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Udemy
-
Machine
Learning
with
Imbalanced
Data
文件列表
01 - Introduction/001 Course Curriculum Overview.mp4
17.5MB
01 - Introduction/002 Course Material.mp4
9.21MB
02 - Machine Learning with Imbalanced Data Overview/001 Imbalanced classes - Introduction.mp4
25.5MB
02 - Machine Learning with Imbalanced Data Overview/002 Nature of the imbalanced class.mp4
24.81MB
02 - Machine Learning with Imbalanced Data Overview/003 Approaches to work with imbalanced datasets - Overview.mp4
12.13MB
03 - Evaluation Metrics/001 Introduction to Performance Metrics.mp4
6.78MB
03 - Evaluation Metrics/002 Accuracy.mp4
11.38MB
03 - Evaluation Metrics/003 Accuracy - Demo.mp4
39.61MB
03 - Evaluation Metrics/004 Precision, Recall and F-measure.mp4
30.24MB
03 - Evaluation Metrics/006 Precision, Recall and F-measure - Demo.mp4
76.09MB
03 - Evaluation Metrics/007 Confusion tables, FPR and FNR.mp4
15.4MB
03 - Evaluation Metrics/008 Confusion tables, FPR and FNR - Demo.mp4
45.98MB
03 - Evaluation Metrics/009 Balanced Accuracy.mp4
7.75MB
03 - Evaluation Metrics/010 Balanced accuracy - Demo.mp4
16.55MB
03 - Evaluation Metrics/011 Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4
11.89MB
03 - Evaluation Metrics/012 Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4
74.3MB
03 - Evaluation Metrics/013 ROC-AUC.mp4
34.31MB
03 - Evaluation Metrics/014 ROC-AUC - Demo.mp4
29.63MB
03 - Evaluation Metrics/015 Precision-Recall Curve.mp4
15.8MB
03 - Evaluation Metrics/016 Precision-Recall Curve - Demo.mp4
18.11MB
03 - Evaluation Metrics/019 Probability.mp4
10.11MB
03 - Evaluation Metrics/020 Metrics for Mutliclass.mp4
26.18MB
03 - Evaluation Metrics/021 Metrics for Multiclass - Demo.mp4
51.95MB
03 - Evaluation Metrics/022 PR and ROC Curves for Multiclass.mp4
11.34MB
03 - Evaluation Metrics/023 PR Curves in Multiclass - Demo.mp4
54.74MB
03 - Evaluation Metrics/024 ROC Curve in Multiclass - Demo.mp4
46.07MB
04 - Udersampling/001 Under-Sampling Methods - Introduction.mp4
31.55MB
04 - Udersampling/002 Random Under-Sampling - Intro.mp4
10.68MB
04 - Udersampling/003 Random Under-Sampling - Demo.mp4
58.66MB
04 - Udersampling/004 Condensed Nearest Neighbours - Intro.mp4
37.56MB
04 - Udersampling/005 Condensed Nearest Neighbours - Demo.mp4
50.03MB
04 - Udersampling/006 Tomek Links - Intro.mp4
9.75MB
04 - Udersampling/007 Tomek Links - Demo.mp4
16.15MB
04 - Udersampling/008 One Sided Selection - Intro.mp4
9.89MB
04 - Udersampling/009 One Sided Selection - Demo.mp4
16.12MB
04 - Udersampling/010 Edited Nearest Neighbours - Intro.mp4
23.49MB
04 - Udersampling/011 Edited Nearest Neighbours - Demo.mp4
26.35MB
04 - Udersampling/012 Repeated Edited Nearest Neighbours - Intro.mp4
13.68MB
04 - Udersampling/013 Repeated Edited Nearest Neighbours - Demo.mp4
19.74MB
04 - Udersampling/014 All KNN - Intro.mp4
13.74MB
04 - Udersampling/015 All KNN - Demo.mp4
36.12MB
04 - Udersampling/016 Neighbourhood Cleaning Rule - Intro.mp4
14.35MB
04 - Udersampling/017 Neighbourhood Cleaning Rule - Demo.mp4
12.47MB
04 - Udersampling/018 NearMiss - Intro.mp4
13.83MB
04 - Udersampling/019 NearMiss - Demo.mp4
18.97MB
04 - Udersampling/020 Instance Hardness Threshold - Intro.mp4
20.53MB
04 - Udersampling/021 Instance Hardness Threshold - Demo.mp4
102.59MB
04 - Udersampling/022 Instance Hardness Threshold Multiclass Demo.mp4
48.3MB
04 - Udersampling/023 Undersampling Method Comparison.mp4
41.11MB
04 - Udersampling/024 Wrapping up the section.mp4
11.7MB
04 - Udersampling/025 Setting up a classifier with under-sampling and cross-validation.mp4
63.88MB
05 - Oversampling/001 Over-Sampling Methods - Introduction.mp4
10.64MB
05 - Oversampling/002 Random Over-Sampling.mp4
21.41MB
05 - Oversampling/003 Random Over-Sampling - Demo.mp4
26.16MB
05 - Oversampling/004 ROS with smoothing - Intro.mp4
23.35MB
05 - Oversampling/005 ROS with smoothing - Demo.mp4
23.41MB
05 - Oversampling/006 SMOTE.mp4
44.61MB
05 - Oversampling/007 SMOTE - Demo.mp4
17.46MB
05 - Oversampling/008 SMOTE-NC.mp4
20.48MB
05 - Oversampling/009 SMOTE-NC - Demo.mp4
18.36MB
05 - Oversampling/010 SMOTE-N.mp4
45.36MB
05 - Oversampling/011 SMOTE-N Demo.mp4
44.8MB
05 - Oversampling/012 ADASYN.mp4
25.24MB
05 - Oversampling/013 ADASYN - Demo.mp4
15.62MB
05 - Oversampling/014 Borderline SMOTE.mp4
34.42MB
05 - Oversampling/015 Borderline SMOTE - Demo.mp4
17.52MB
05 - Oversampling/016 SVM SMOTE.mp4
82.31MB
05 - Oversampling/018 SVM SMOTE - Demo.mp4
35.69MB
05 - Oversampling/019 K-Means SMOTE.mp4
29.82MB
05 - Oversampling/020 K-Means SMOTE - Demo.mp4
18.58MB
05 - Oversampling/021 Over-Sampling Method Comparison.mp4
26.92MB
05 - Oversampling/022 Wrapping up the section.mp4
27.24MB
05 - Oversampling/023 How to Correctly Set Up a Classifier with Over-sampling.mp4
28.01MB
05 - Oversampling/024 Setting Up a Classifier - Demo.mp4
16.03MB
06 - Over and Undersampling/001 Combining Over and Under-sampling - Intro.mp4
30.49MB
06 - Over and Undersampling/002 Combining Over and Under-sampling - Demo.mp4
26.45MB
06 - Over and Undersampling/003 Comparison of Over and Under-sampling Methods.mp4
32.08MB
06 - Over and Undersampling/005 Wrapping up.mp4
7.53MB
07 - Ensemble Methods/001 Ensemble methods with Imbalanced Data.mp4
13.44MB
07 - Ensemble Methods/002 Foundations of Ensemble Learning.mp4
9.58MB
07 - Ensemble Methods/003 Bagging.mp4
8.83MB
07 - Ensemble Methods/004 Bagging plus Over- or Under-Sampling.mp4
36.96MB
07 - Ensemble Methods/005 Boosting.mp4
26.8MB
07 - Ensemble Methods/006 Boosting plus Re-Sampling.mp4
41.63MB
07 - Ensemble Methods/007 Hybdrid Methods.mp4
11.99MB
07 - Ensemble Methods/008 Ensemble Methods - Demo.mp4
31.29MB
07 - Ensemble Methods/009 Wrapping up.mp4
26.11MB
08 - Cost Sensitive Learning/001 Cost-sensitive Learning - Intro.mp4
15.45MB
08 - Cost Sensitive Learning/002 Types of Cost.mp4
35.35MB
08 - Cost Sensitive Learning/003 Obtaining the Cost.mp4
9.22MB
08 - Cost Sensitive Learning/004 Cost Sensitive Approaches.mp4
5.25MB
08 - Cost Sensitive Learning/005 Misclassification Cost in Logistic Regression.mp4
9.77MB
08 - Cost Sensitive Learning/006 Misclassification Cost in Decision Trees.mp4
9.73MB
08 - Cost Sensitive Learning/007 Cost Sensitive Learning with Scikit-learn.mp4
53.6MB
08 - Cost Sensitive Learning/008 Find Optimal Cost with hyperparameter tuning.mp4
20.18MB
08 - Cost Sensitive Learning/009 Bayes Conditional Risk.mp4
42.6MB
08 - Cost Sensitive Learning/010 MetaCost.mp4
33.98MB
08 - Cost Sensitive Learning/011 MetaCost - Demo.mp4
17.7MB
08 - Cost Sensitive Learning/012 Optional MetaCost Base Code.mp4
30.41MB
09 - Probability Calibration/001 Probability Calibration.mp4
15.19MB
09 - Probability Calibration/002 Probability Calibration Curves.mp4
13.64MB
09 - Probability Calibration/003 Probability Calibration Curves - Demo.mp4
61.02MB
09 - Probability Calibration/004 Brier Score.mp4
7.27MB
09 - Probability Calibration/005 Brier Score - Demo.mp4
42.81MB
09 - Probability Calibration/006 Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4
15.8MB
09 - Probability Calibration/007 Calibrating a Classifier.mp4
21.16MB
09 - Probability Calibration/008 Calibrating a Classifier - Demo.mp4
44.44MB
09 - Probability Calibration/009 Calibrating a Classfiier after SMOTE or Under-sampling.mp4
45.79MB
09 - Probability Calibration/010 Calibrating a Classifier with Cost-sensitive Learning.mp4
21.99MB
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