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

## Feature engineering is important

Feature engineering is a crucial stage in the entire process of any machine learning project. Its main purpose is to extract features from the raw data via different methods. For a long time, feature engineering has been an indispensable part of machine learning. Whether some distinguishing features can be constructed or not, machine learning is important for the final model effect.

In this course, we will focus on the sub-topic of feature engineering: creates new features from existing features/fields/columns. 

> Coming up with features is difficult, time-consuming, and requires expert knowledge. 'Applied machine learning' is basically feature engineering. -- **Andrew Ng**

## What can you learn from this course?

- You will learn how to create new features by combining two or more features.
- You will learn how to create new features from the UNIX timestamp.
- You will learn how to encode the category features in different ways.

## Prerequisites

We hope you have some basic knowledge of Python and machine learning. If not, it doesn't matter.

>**Notice:** 
> If you are not familiar with `sklearn`, you can check the course [Hands-on Machine Learning with Scikit-Learn](https://www.educative.io/courses/hands-on-machine-learning-with-scikit-learn).
>
>If you are not familiar with `pandas`, you can check the course [Data Analysis & Processing with Pandas](https://www.educative.io/courses/data-analysis-processing-with-pandas).

# Feature engineering is important

Feature engineering is a crucial stage in the entire process of any machine learning project. Its main purpose is to extract features from the raw data via different methods. For a long time, feature engineering has been an indispensable part of machine learning. Whether some distinguishing features can be constructed or not, machine learning is important for the final model effect.

In this course, we will focus on the sub-topic of feature engineering: creates new features from existing features/fields/columns. 

> Coming up with features is difficult, time-consuming, and requires expert knowledge. 'Applied machine learning' is basically feature engineering. -- **Andrew Ng**

# What can you learn from this course?

- You will learn how to create new features by combining two or more features.
- You will learn how to create new features from the UNIX timestamp.
- You will learn how to encode the category features in different ways.

# Prerequisites

We hope you have some basic knowledge of Python and machine learning. If not, it doesn't matter.

>**Notice:** 
> If you are not familiar with `sklearn`, you can check the course [Hands-on Machine Learning with Scikit-Learn](https://www.educative.io/courses/hands-on-machine-learning-with-scikit-learn).
>
>If you are not familiar with `pandas`, you can check the course [Data Analysis & Processing with Pandas](https://www.educative.io/courses/data-analysis-processing-with-pandas).

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About This Course

Feature engineering is important

What can you learn from this course?

Prerequisites