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.

If you are not familiar with pandas, you can check the course Data Analysis & Processing with Pandas.

Get hands-on with 1200+ tech skills courses.