Machine Learning with Imbalanced Data

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

What you'll learn

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

Requirements

Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours
Python programming, including familiarity with NumPy, Pandas and Scikit-learn

Description

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.

Who this course is for:

Data Scientists and Machine Learning engineers working with imbalanced datasets