Section 1 : Introduction
Lecture 1 Introduction copy 00:02:53 Duration
Lecture 2 Course Curriculum Overview 00:02:54 Duration
Lecture 3 Course Material 00:01:47 Duration
Lecture 4 Code Jupyter notebooks
Lecture 5 Presentations covered in the course
Lecture 6 Python package Imbalanced-learn
Lecture 7 Download Datasets
Lecture 8 Additional resources for Machine Learning and Python programming

Section 2 : Machine Learning with Imbalanced Data Overview
Lecture 1 Imbalanced classes - Introduction 00:04:53 Duration
Lecture 2 Nature of the imbalanced class 00:04:38 Duration
Lecture 3 Approaches to work with imbalanced datasets - Overview 00:03:45 Duration
Lecture 4 Additional Reading Resources (Optional)

Section 3 : Evaluation Metrics
Lecture 1 Introduction to Performance Metrics 00:02:28 Duration
Lecture 2 Accuracy 00:04:23 Duration
Lecture 3 Accuracy - Demo 00:06:05 Duration
Lecture 4 Precision, Recall and F-measure 00:13:32 Duration
Lecture 5 Install Yellowbrick
Lecture 6 Precision, Recall and F-measure - Demo 00:10:04 Duration
Lecture 7 Confusion tables, FPR and FNR 00:05:46 Duration
Lecture 8 Confusion tables, FPR and FNR - Demo 00:07:32 Duration
Lecture 9 Geometric Mean, Dominance, Index of Imbalanced Accuracy 00:04:13 Duration
Lecture 10 Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo 00:10:25 Duration
Lecture 11 ROC-AUC 00:07:12 Duration
Lecture 12 ROC-AUC - Demo 00:04:46 Duration
Lecture 13 Precision-Recall Curve 00:07:48 Duration
Lecture 14 Precision-Recall Curve - Demo 00:02:40 Duration
Lecture 15 Additional reading resources (Optional)
Lecture 16 Probability 00:04:01 Duration

Section 4 : Udersampling
Lecture 1 Under-Sampling Methods - Introduction 00:05:09 Duration
Lecture 2 Random Under-Sampling - Intro 00:05:29 Duration
Lecture 3 Random Under-Sampling - Demo 00:10:11 Duration
Lecture 4 Condensed Nearest Neighbours - Intro 00:07:15 Duration
Lecture 5 Condensed Nearest Neighbours - Demo
Lecture 6 Tomek Links - Intro
Lecture 7 Tomek Links - Demo 00:03:18 Duration
Lecture 8 One Sided Selection - Intro 00:02:26 Duration
Lecture 9 One Sided Selection - Demo 00:03:32 Duration
Lecture 10 Edited Nearest Neighbours - Intro 00:04:35 Duration
Lecture 11 Edited Nearest Neighbours - Demo 00:04:02 Duration
Lecture 12 Repeated Edited Nearest Neighbours - Intro 00:04:07 Duration
Lecture 13 Repeated Edited Nearest Neighbours - Demo 00:03:00 Duration
Lecture 14 All KNN - Intro 00:03:18 Duration
Lecture 15 All KNN - Demo 00:02:54 Duration
Lecture 16 Neighbourhood Cleaning Rule - Intro 00:04:05 Duration
Lecture 17 Neighbourhood Cleaning Rule - Demo 00:02:03 Duration
Lecture 18 NearMiss - Intro 00:03:41 Duration
Lecture 19 NearMiss - Demo 00:03:53 Duration
Lecture 20 Instance Hardness Threshold - Intro 00:03:57 Duration
Lecture 21 Instance Hardness Threshold - Demo 00:03:41 Duration
Lecture 22 Undersampling Method Comparison 00:07:44 Duration
Lecture 23 Summary Table

Section 5 : Oversampling
Lecture 1 Over-Sampling Methods - Introduction 00:03:31 Duration
Lecture 2 Random Over-Sampling
Lecture 3 Random Over-Sampling - Demo 00:04:55 Duration
Lecture 4 SMOTE 00:09:19 Duration
Lecture 5 SMOTE - Demo 00:02:35 Duration
Lecture 6 SMOTE-NC 00:08:52 Duration
Lecture 7 SMOTE-NC - Demo 00:02:56 Duration
Lecture 8 ADASYN 00:07:05 Duration
Lecture 9 ADASYN - Demo
Lecture 10 Borderline SMOTE 00:08:31 Duration
Lecture 11 Borderline SMOTE - Demo 00:03:13 Duration
Lecture 12 SVM SMOTE 00:05:28 Duration
Lecture 13 SVM SMOTE - Demo 00:04:32 Duration
Lecture 14 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 15 K-Means SMOTE - Demo 00:03:29 Duration
Lecture 16 Over-Sampling Method Comparison 00:06:18 Duration

Section 6 : Over and Undersampling
Lecture 1 Combining Over and Under-sampling - Intro 00:06:16 Duration
Lecture 2 Combining Over and Under-sampling - Demo 00:05:26 Duration
Lecture 3 Comparison of Over and Under-sampling Methods 00:05:54 Duration

Section 7 : Ensemble Methods
Lecture 1 Ensemble Methods - Coming soon

Section 8 : Cost Sensitive Learning
Lecture 1 Cost-sensitive Learning - Intro 00:07:04 Duration
Lecture 2 Types of Cost 00:10:31 Duration
Lecture 3 Obtaining the Cost 00:04:20 Duration
Lecture 4 Cost Sensitive Approaches 00:01:44 Duration
Lecture 5 Misclassification Cost in Logistic Regression 00:02:50 Duration
Lecture 6 Misclassification Cost in Decision Trees 00:02:52 Duration
Lecture 7 Cost Sensitive Learning with Scikit-learn- Demo 00:07:13 Duration
Lecture 8 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 9 Bayes Conditional Risk 00:13:35 Duration
Lecture 10 MetaCost 00:07:51 Duration
Lecture 11 MetaCost - Demo 00:03:40 Duration
Lecture 12 Optional MetaCost Base Code 00:06:39 Duration
Lecture 13 Additional Reading Resources

Section 9 : Probability Calibration
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 Probability Calibration Curves 00:05:40 Duration
Lecture 3 Probability Calibration Curves - Demo 00:09:37 Duration
Lecture 4 Brier Score 00:02:53 Duration
Lecture 5 Brier Score - Demo 00:07:07 Duration
Lecture 6 Under- and Over-sampling and Cost-sensitive learning on Probability Calibration 00:04:32 Duration
Lecture 7 Calibrating a Classifier 00:05:14 Duration
Lecture 8 Calibrating a Classifier - Demo 00:06:20 Duration
Lecture 9 Calibrating a Classfiier after SMOTE or Under-sampling 00:08:05 Duration
Lecture 10 Calibrating a Classifier with Cost-sensitive Learning
Lecture 11 Probability Additional reading resources

Section 10 : Moving Forward
Lecture 1 Next steps