Feature engineering is a crucial stage in any machine learning project. It allows you to use data to define features that enable machine learning algorithms to work properly. In this course, you will learn the techniques that will help you create new features from existing features. You’ll start by diving into label encoding which is crucial for converting categorical features into numerical. You’ll also learn about other various types of encoding such as: one-hot, count, and mean, all of which are important for feature engineering. In the remaining chapters, you’ll learn about feature interaction and datetime features. In all, this course will show you the many different ways you can create features from existing ones.

ABOUT THE AUTHOR

Neko Yan

Data lovers, machine learning enthusiasts. Senior machine learning researcher in NLP, reinforcement learning, and display advertisement prediction.

Learn more about Neko

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