Machine learning has become a commonplace topic in our day-to-day lives. The advancement in the field has been so dramatic that today, even cell phones incorporate advanced machine learning and artificial intelligence-related facilities, capable of responding and taking actions based on human instructions.

A subject that was once limited to university classrooms has transformed into a full-fledged industry, pervading our daily lives in ways we could not have envisioned even just a few years ago.

The aim of this chapter is to introduce the reader to the underpinnings of machine learning and explain the concepts in simple, lucid terms that will help readers become familiar with the core ideas in the subject. We'll start off with a high-level overview of machine learning, and explain the different categories and how to distinguish them. We'll explain some of the salient concepts in machine learning, such as data pre-processing, feature engineering, and variable importance. The next chapter will go into more detail regarding individual algorithms and theoretical machine learning.

We'll conclude with exercises that leverage real-world datasets to perform machine learning operations using R.

We will cover the following topics in this chapter:

• What is machine learning?
• The popular emergence
• Machine learning, statistics, and artificial intelligence (AI)
• Categories of machine learning
• Core concepts in machine learning
• Machine learning tutorial