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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

We should interact with every feature.

If we want to convert a UNIX timestamp (1529720105) to a pandas Datetime object, what unit should we use?

If we want to convert a UNIX timestamp (1529720105) to a pandas Datetime object, what unit should we use?

Which of the following statistics can be used as encoding?

Which of the following statistics can be used as encoding?

Is the number generated by label encoding comparable?

Is the number generated by label encoding comparable?

Count features work well with tree-based models.

Count features work well with tree-based models.

The label encoding operation must be performed on both the train and test dataset at the same time.

The label encoding operation must be performed on both the train and test dataset at the same time.

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Quiz