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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.

Feature Engineering for Machine Learning

# What is feature interaction?

Feature interaction is a method for new features by interacting with two or more existing features. In short, if you have two features of category types, you can create a new feature by joining them together, as shown in the image below.


Learn how to add new features by feature interaction.

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Feature Interaction

What is feature interaction?