Feature Engineering for Existing Features.png

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 one-hot encoding?

**One-hot encoding** is another method to convert categorical features into numerical features. Unlike the [label encoding](https://www.educative.io/courses/feature-engineering-for-machine-learning/one-hot-encoding) methods we saw in the previous lesson, **one-hot** converts each categorical value into a new column and assigns a value of **1** or **0** to the columns. This is shown in the image below:


# What is one-hot encoding?

**One-hot encoding** is another method to convert categorical features into numerical features. Unlike the [label encoding](https://www.educative.io/courses/feature-engineering-for-machine-learning/one-hot-encoding) methods we saw in the previous lesson, **one-hot** converts each categorical value into a new column and assigns a value of **1** or **0** to the columns. This is shown in the image below:


 Learn how to use one-hot encoding for the categorical features.

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One-hot Encoding

What is one-hot encoding?