The Learning Process

In the first chapter, we saw a general overview of the mathematical concepts, history, and areas of the field of machine learning. 

As this book intends to provide a practical but formally correct way of learning, now it's time to explore the general thought process for any machine learning process. These concepts will be pervasive throughout the chapters and will help us to define a common framework of the best practices of the field. 

The topics we will cover in this chapter are as follows:

• Understanding the problem and definitions
• Dataset retrieval, preprocessing, and feature engineering
• Model definition, training, and evaluation
• Understanding results and metrics

Every machine learning problem tends to have its own particularities. Nevertheless, as the discipline advances through time, there are emerging patterns of what kind of steps a machine learning process should include, and the best practices for them. The following sections will be a list of these steps, including code examples for the cases that apply.