4.79 out of 5

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Machine Learning with Imbalanced Data

Learn multiple techniques to tackle data imbalance and improve the performance of your machine learning models.

Instructor:

Soledad Galli

2,463 students enrolled

English [Auto]

Under-sampling methods at random

Under-sampling methods which focus on observations that are harder to classify

Under-sampling methods that ignore potentially noisy observations

Over-sampling methods to increase the number of minority observations

Ways of creating syntethic data to increase the examples of the minority class

SMOTE and its variants

Use ensemble methods with sampling techniques to improve model performance

The most suitable evaluation metrics to use with imbalanced datasets

Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models.

If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique. Specifically, you will learn:

Under-sampling methods at random or focused on highlighting certain sample populations
Over-sampling methods at random and those which create new examples based of existing observations
Ensemble methods that leverage the power of multiple weak learners in conjunction with sampling techniques to boost model performance
Cost sensitive methods which penalize wrong decisions more severely for minority classes
The appropriate metrics to evaluate model performance on imbalanced datasets

By the end of the course, you will be able to decide which technique is suitable for your dataset, and / or apply and compare the improvement in performance returned by the different methods on multiple datasets.

This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

In addition, the code is updated regularly to keep up with new trends and new Python library releases.

So what are you waiting for? Enroll today, learn how to work with imbalanced datasets and build better machine learning models.

Introduction

Course Curriculum Overview

Course Material

Code | Jupyter notebooks

Presentations covered in the course

Python package Imbalanced-learn

Download Datasets

Additional resources for Machine Learning and Python programming

Machine Learning with Imbalanced Data: Overview

Imbalanced classes - Introduction

Nature of the imbalanced class

Approaches to work with imbalanced datasets - Overview

Additional Reading Resources (Optional)

Evaluation Metrics

Introduction to Performance Metrics

Accuracy

Accuracy - Demo

Precision, Recall and F-measure

Install Yellowbrick

Precision, Recall and F-measure - Demo

Confusion tables, FPR and FNR

Confusion tables, FPR and FNR - Demo

Geometric Mean, Dominance, Index of Imbalanced Accuracy

Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo

ROC-AUC

ROC-AUC - Demo

Precision-Recall Curve

Precision-Recall Curve - Demo

Additional reading resources (Optional)

Probability

Udersampling

Under-Sampling Methods - Introduction

Random Under-Sampling - Intro

Random Under-Sampling - Demo

Condensed Nearest Neighbours - Intro

Condensed Nearest Neighbours - Demo

Tomek Links - Intro

Tomek Links - Demo

One Sided Selection - Intro

One Sided Selection - Demo

Edited Nearest Neighbours - Intro

Edited Nearest Neighbours - Demo

Repeated Edited Nearest Neighbours - Intro

Repeated Edited Nearest Neighbours - Demo

All KNN - Intro

All KNN - Demo

Neighbourhood Cleaning Rule - Intro

Neighbourhood Cleaning Rule - Demo

NearMiss - Intro

NearMiss - Demo

Instance Hardness Threshold - Intro

Instance Hardness Threshold - Demo

Undersampling Method Comparison

Summary Table

Oversampling

Over-Sampling Methods - Introduction

Random Over-Sampling

Random Over-Sampling - Demo

SMOTE

SMOTE - Demo

SMOTE-NC

SMOTE-NC - Demo

ADASYN

ADASYN - Demo

Borderline SMOTE

Borderline SMOTE - Demo

SVM SMOTE

SVM SMOTE - Demo

K-Means SMOTE

K-Means SMOTE - Demo

Over-Sampling Method Comparison

Over and Undersampling

Combining Over and Under-sampling - Intro

Combining Over and Under-sampling - Demo

Comparison of Over and Under-sampling Methods

Ensemble Methods

Ensemble methods with Imbalanced Data

Foundations of Ensemble Learning

Bagging

Bagging plus Over- or Under-Sampling

Boosting

Boosting plus Re-Sampling

Hybdrid Methods

Ensemble Methods - Demo

Additional Reading Resources

Cost Sensitive Learning

Cost-sensitive Learning - Intro

Types of Cost

Obtaining the Cost

Cost Sensitive Approaches

Misclassification Cost in Logistic Regression

Misclassification Cost in Decision Trees

Cost Sensitive Learning with Scikit-learn- Demo

Find Optimal Cost with hyperparameter tuning

Bayes Conditional Risk

MetaCost

MetaCost - Demo

Optional: MetaCost Base Code

Additional Reading Resources

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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Machine Learning with Imbalanced Data

Introduction

Machine Learning with Imbalanced Data: Overview

Evaluation Metrics

Udersampling

Oversampling

Over and Undersampling

Ensemble Methods

Cost Sensitive Learning

How long do I have access to the course materials?

Can I take my courses with me wherever I go?

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