One of the most important steps in the machine learning process is understanding and preparing data. Before you can learn to train models, you need to ensure the data selected for your model is appropriate to solve the problem.

In this course, you will focus on taking raw data, analyzing and organizing it, and preparing it for the next stage of the machine learning process: modeling. You will practice identifying examples, along with their features and labels, to prepare for supervised learning. You will also practice organizing your data into a data matrix. You will learn about feature engineering, which will allow you to transform your data into a format that is most appropriate for your specific model. By the end of the course, you will be set up with the necessary foundations for managing data in ML.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Machine Learning Foundations