Section 1 : Introduction

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

Section 2 : Machine Learning with Imbalanced Data Overview

Lecture 9 Imbalanced classes - Introduction 4:53
Lecture 10 Nature of the imbalanced class 4:38
Lecture 11 Approaches to work with imbalanced datasets - Overview 3:45
Lecture 12 Additional Reading Resources (Optional) Pdf

Section 3 : Evaluation Metrics

Lecture 13 Introduction to Performance Metrics 2:28
Lecture 14 Accuracy 4:23
Lecture 15 Accuracy - Demo 6:5
Lecture 16 Precision, Recall and F-measure 13:32
Lecture 17 Install Yellowbrick Text
Lecture 18 Precision, Recall and F-measure - Demo 10:4
Lecture 19 Confusion tables, FPR and FNR 5:46
Lecture 20 Confusion tables, FPR and FNR - Demo 7:32
Lecture 21 Geometric Mean, Dominance, Index of Imbalanced Accuracy 4:13
Lecture 22 Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo 10:25
Lecture 23 ROC-AUC 7:12
Lecture 24 ROC-AUC - Demo 4:46
Lecture 25 Precision-Recall Curve 7:48
Lecture 26 Precision-Recall Curve - Demo 2:40
Lecture 27 Additional reading resources (Optional) Pdf
Lecture 28 Probability 4:1

Section 4 : Udersampling

Lecture 29 Under-Sampling Methods - Introduction 5:9
Lecture 30 Random Under-Sampling - Intro 5:29
Lecture 31 Random Under-Sampling - Demo 10:11
Lecture 32 Condensed Nearest Neighbours - Intro 7:15
Lecture 33 Condensed Nearest Neighbours - Demo
Lecture 34 Tomek Links - Intro
Lecture 35 Tomek Links - Demo 3:18
Lecture 36 One Sided Selection - Intro 2:26
Lecture 37 One Sided Selection - Demo 3:32
Lecture 38 Edited Nearest Neighbours - Intro 4:35
Lecture 39 Edited Nearest Neighbours - Demo 4:2
Lecture 40 Repeated Edited Nearest Neighbours - Intro 4:7
Lecture 41 Repeated Edited Nearest Neighbours - Demo 3:0
Lecture 42 All KNN - Intro 3:18
Lecture 43 All KNN - Demo 2:54
Lecture 44 Neighbourhood Cleaning Rule - Intro 4:5
Lecture 45 Neighbourhood Cleaning Rule - Demo 2:3
Lecture 46 NearMiss - Intro 3:41
Lecture 47 NearMiss - Demo 3:53
Lecture 48 Instance Hardness Threshold - Intro 3:57
Lecture 49 Instance Hardness Threshold - Demo 3:41
Lecture 50 Undersampling Method Comparison 7:44
Lecture 51 Summary Table Text

Section 5 : Oversampling

Lecture 52 Over-Sampling Methods - Introduction 3:31
Lecture 53 Random Over-Sampling
Lecture 54 Random Over-Sampling - Demo 4:55
Lecture 55 SMOTE 9:19
Lecture 56 SMOTE - Demo 2:35
Lecture 57 SMOTE-NC 8:52
Lecture 58 SMOTE-NC - Demo 2:56
Lecture 59 ADASYN 7:5
Lecture 60 ADASYN - Demo
Lecture 61 Borderline SMOTE 8:31
Lecture 62 Borderline SMOTE - Demo 3:13
Lecture 63 SVM SMOTE 5:28
Lecture 64 SVM SMOTE - Demo 4:32
Lecture 65 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 66 K-Means SMOTE - Demo 3:29
Lecture 67 Over-Sampling Method Comparison 6:18

Section 6 : Over and Undersampling

Lecture 68 Combining Over and Under-sampling - Intro 6:16
Lecture 69 Combining Over and Under-sampling - Demo 5:26
Lecture 70 Comparison of Over and Under-sampling Methods 5:54

Section 7 : Ensemble Methods

Lecture 71 Ensemble Methods - Coming soon Text

Section 8 : Cost Sensitive Learning

Lecture 72 Cost-sensitive Learning - Intro 7:4
Lecture 73 Types of Cost 10:31
Lecture 74 Obtaining the Cost 4:20
Lecture 75 Cost Sensitive Approaches 1:44
Lecture 76 Misclassification Cost in Logistic Regression 2:50
Lecture 77 Misclassification Cost in Decision Trees 2:52
Lecture 78 Cost Sensitive Learning with Scikit-learn- Demo 7:13
Lecture 79 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 80 Bayes Conditional Risk 13:35
Lecture 81 MetaCost 7:51
Lecture 82 MetaCost - Demo 3:40
Lecture 83 Optional MetaCost Base Code 6:39
Lecture 84 Additional Reading Resources Pdf

Section 9 : Probability Calibration

Lecture 85 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 86 Probability Calibration Curves 5:40
Lecture 87 Probability Calibration Curves - Demo 9:37
Lecture 88 Brier Score 2:53
Lecture 89 Brier Score - Demo 7:7
Lecture 90 Under- and Over-sampling and Cost-sensitive learning on Probability Calibration 4:32
Lecture 91 Calibrating a Classifier 5:14
Lecture 92 Calibrating a Classifier - Demo 6:20
Lecture 93 Calibrating a Classfiier after SMOTE or Under-sampling 8:5
Lecture 94 Calibrating a Classifier with Cost-sensitive Learning
Lecture 95 Probability Additional reading resources Pdf

Section 10 : Moving Forward

Lecture 96 Next steps Text