Machine Learning with Imbalanced Data

Machine Learning with Imbalanced Data

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 more than 10 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.

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

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What you will learn

• Apply random under-sampling to remove observations from majority classes
• Perform under-sampling by removing observations that are hard to classify
• Carry out under-sampling by retaining observations at the boundary of class separation

Rating: 4.75833

Level: Intermediate Level

Duration: 11.5 hours

Instructor: Soledad Galli