种子简介

种子名称： [FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data
文件类型： 视频
文件数目： 109个文件
文件大小： 2.9 GB
收录时间： 2022-11-9 08:42
已经下载： 3次
资源热度： 141
最近下载： 2024-11-24 13:20

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:3b0af3c432a8688ab0e4c39969d7994950063be3&dn=[FreeCourseSite.com] Udemy - Machine Learning with Imbalanced Data 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

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

01 - Introduction/001 Course Curriculum Overview.mp417.5MB

01 - Introduction/002 Course Material.mp49.21MB

02 - Machine Learning with Imbalanced Data Overview/001 Imbalanced classes - Introduction.mp425.5MB

02 - Machine Learning with Imbalanced Data Overview/002 Nature of the imbalanced class.mp424.81MB

02 - Machine Learning with Imbalanced Data Overview/003 Approaches to work with imbalanced datasets - Overview.mp412.13MB

03 - Evaluation Metrics/001 Introduction to Performance Metrics.mp46.78MB

03 - Evaluation Metrics/002 Accuracy.mp411.38MB

03 - Evaluation Metrics/003 Accuracy - Demo.mp439.61MB

03 - Evaluation Metrics/004 Precision, Recall and F-measure.mp430.24MB

03 - Evaluation Metrics/006 Precision, Recall and F-measure - Demo.mp476.09MB

03 - Evaluation Metrics/007 Confusion tables, FPR and FNR.mp415.4MB

03 - Evaluation Metrics/008 Confusion tables, FPR and FNR - Demo.mp445.98MB

03 - Evaluation Metrics/009 Balanced Accuracy.mp47.75MB

03 - Evaluation Metrics/010 Balanced accuracy - Demo.mp416.55MB

03 - Evaluation Metrics/011 Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp411.89MB

03 - Evaluation Metrics/012 Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp474.3MB

03 - Evaluation Metrics/013 ROC-AUC.mp434.31MB

03 - Evaluation Metrics/014 ROC-AUC - Demo.mp429.63MB

03 - Evaluation Metrics/015 Precision-Recall Curve.mp415.8MB

03 - Evaluation Metrics/016 Precision-Recall Curve - Demo.mp418.11MB

03 - Evaluation Metrics/019 Probability.mp410.11MB

03 - Evaluation Metrics/020 Metrics for Mutliclass.mp426.18MB

03 - Evaluation Metrics/021 Metrics for Multiclass - Demo.mp451.95MB

03 - Evaluation Metrics/022 PR and ROC Curves for Multiclass.mp411.34MB

03 - Evaluation Metrics/023 PR Curves in Multiclass - Demo.mp454.74MB

03 - Evaluation Metrics/024 ROC Curve in Multiclass - Demo.mp446.07MB

04 - Udersampling/001 Under-Sampling Methods - Introduction.mp431.55MB

04 - Udersampling/002 Random Under-Sampling - Intro.mp410.68MB

04 - Udersampling/003 Random Under-Sampling - Demo.mp458.66MB

04 - Udersampling/004 Condensed Nearest Neighbours - Intro.mp437.56MB

04 - Udersampling/005 Condensed Nearest Neighbours - Demo.mp450.03MB

04 - Udersampling/006 Tomek Links - Intro.mp49.75MB

04 - Udersampling/007 Tomek Links - Demo.mp416.15MB

04 - Udersampling/008 One Sided Selection - Intro.mp49.89MB

04 - Udersampling/009 One Sided Selection - Demo.mp416.12MB

04 - Udersampling/010 Edited Nearest Neighbours - Intro.mp423.49MB

04 - Udersampling/011 Edited Nearest Neighbours - Demo.mp426.35MB

04 - Udersampling/012 Repeated Edited Nearest Neighbours - Intro.mp413.68MB

04 - Udersampling/013 Repeated Edited Nearest Neighbours - Demo.mp419.74MB

04 - Udersampling/014 All KNN - Intro.mp413.74MB

04 - Udersampling/015 All KNN - Demo.mp436.12MB

04 - Udersampling/016 Neighbourhood Cleaning Rule - Intro.mp414.35MB

04 - Udersampling/017 Neighbourhood Cleaning Rule - Demo.mp412.47MB

04 - Udersampling/018 NearMiss - Intro.mp413.83MB

04 - Udersampling/019 NearMiss - Demo.mp418.97MB

04 - Udersampling/020 Instance Hardness Threshold - Intro.mp420.53MB

04 - Udersampling/021 Instance Hardness Threshold - Demo.mp4102.59MB

04 - Udersampling/022 Instance Hardness Threshold Multiclass Demo.mp448.3MB

04 - Udersampling/023 Undersampling Method Comparison.mp441.11MB

04 - Udersampling/024 Wrapping up the section.mp411.7MB

04 - Udersampling/025 Setting up a classifier with under-sampling and cross-validation.mp463.88MB

05 - Oversampling/001 Over-Sampling Methods - Introduction.mp410.64MB

05 - Oversampling/002 Random Over-Sampling.mp421.41MB

05 - Oversampling/003 Random Over-Sampling - Demo.mp426.16MB

05 - Oversampling/004 ROS with smoothing - Intro.mp423.35MB

05 - Oversampling/005 ROS with smoothing - Demo.mp423.41MB

05 - Oversampling/006 SMOTE.mp444.61MB

05 - Oversampling/007 SMOTE - Demo.mp417.46MB

05 - Oversampling/008 SMOTE-NC.mp420.48MB

05 - Oversampling/009 SMOTE-NC - Demo.mp418.36MB

05 - Oversampling/010 SMOTE-N.mp445.36MB

05 - Oversampling/011 SMOTE-N Demo.mp444.8MB

05 - Oversampling/012 ADASYN.mp425.24MB

05 - Oversampling/013 ADASYN - Demo.mp415.62MB

05 - Oversampling/014 Borderline SMOTE.mp434.42MB

05 - Oversampling/015 Borderline SMOTE - Demo.mp417.52MB

05 - Oversampling/016 SVM SMOTE.mp482.31MB

05 - Oversampling/018 SVM SMOTE - Demo.mp435.69MB

05 - Oversampling/019 K-Means SMOTE.mp429.82MB

05 - Oversampling/020 K-Means SMOTE - Demo.mp418.58MB

05 - Oversampling/021 Over-Sampling Method Comparison.mp426.92MB

05 - Oversampling/022 Wrapping up the section.mp427.24MB

05 - Oversampling/023 How to Correctly Set Up a Classifier with Over-sampling.mp428.01MB

05 - Oversampling/024 Setting Up a Classifier - Demo.mp416.03MB

06 - Over and Undersampling/001 Combining Over and Under-sampling - Intro.mp430.49MB

06 - Over and Undersampling/002 Combining Over and Under-sampling - Demo.mp426.45MB

06 - Over and Undersampling/003 Comparison of Over and Under-sampling Methods.mp432.08MB

06 - Over and Undersampling/005 Wrapping up.mp47.53MB

07 - Ensemble Methods/001 Ensemble methods with Imbalanced Data.mp413.44MB

07 - Ensemble Methods/002 Foundations of Ensemble Learning.mp49.58MB

07 - Ensemble Methods/003 Bagging.mp48.83MB

07 - Ensemble Methods/004 Bagging plus Over- or Under-Sampling.mp436.96MB

07 - Ensemble Methods/005 Boosting.mp426.8MB

07 - Ensemble Methods/006 Boosting plus Re-Sampling.mp441.63MB

07 - Ensemble Methods/007 Hybdrid Methods.mp411.99MB

07 - Ensemble Methods/008 Ensemble Methods - Demo.mp431.29MB

07 - Ensemble Methods/009 Wrapping up.mp426.11MB

08 - Cost Sensitive Learning/001 Cost-sensitive Learning - Intro.mp415.45MB

08 - Cost Sensitive Learning/002 Types of Cost.mp435.35MB

08 - Cost Sensitive Learning/003 Obtaining the Cost.mp49.22MB

08 - Cost Sensitive Learning/004 Cost Sensitive Approaches.mp45.25MB

08 - Cost Sensitive Learning/005 Misclassification Cost in Logistic Regression.mp49.77MB

08 - Cost Sensitive Learning/006 Misclassification Cost in Decision Trees.mp49.73MB

08 - Cost Sensitive Learning/007 Cost Sensitive Learning with Scikit-learn.mp453.6MB

08 - Cost Sensitive Learning/008 Find Optimal Cost with hyperparameter tuning.mp420.18MB

08 - Cost Sensitive Learning/009 Bayes Conditional Risk.mp442.6MB

08 - Cost Sensitive Learning/010 MetaCost.mp433.98MB

08 - Cost Sensitive Learning/011 MetaCost - Demo.mp417.7MB

08 - Cost Sensitive Learning/012 Optional MetaCost Base Code.mp430.41MB

09 - Probability Calibration/001 Probability Calibration.mp415.19MB

09 - Probability Calibration/002 Probability Calibration Curves.mp413.64MB

09 - Probability Calibration/003 Probability Calibration Curves - Demo.mp461.02MB

09 - Probability Calibration/004 Brier Score.mp47.27MB

09 - Probability Calibration/005 Brier Score - Demo.mp442.81MB

09 - Probability Calibration/006 Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp415.8MB

09 - Probability Calibration/007 Calibrating a Classifier.mp421.16MB

09 - Probability Calibration/008 Calibrating a Classifier - Demo.mp444.44MB

09 - Probability Calibration/009 Calibrating a Classfiier after SMOTE or Under-sampling.mp445.79MB

09 - Probability Calibration/010 Calibrating a Classifier with Cost-sensitive Learning.mp421.99MB