4.79 out of 5
4.79
198 reviews on Udemy

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.

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

8 hours on-demand video
13 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion
Machine Learning with Imbalanced Data
Price:
$218.98 $169

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