Machine learning is a sub field of artificial intelligence (AI) focused on the aim of developing algorithms and techniques that enable computers to learn from massive amounts of data. Given the increasing rate at which data is produced, machine learning has played a critical role in solving difficult problems in recent years. This success was the main driving force behind the funding and development of many great machine learning libraries that make use of data in order to build predictive models. Furthermore, businesses have started to realize the potential of machine learning, driving the demand for data scientists and machine learning engineers to new heights, in order to design better-performing predictive models.

This chapter serves as a refresher on the main concepts and terminology, as well as an introduction to the frameworks that will be used throughout the book, in order to approach ensemble learning with a solid foundation.

The main topics covered in this chapter are the following:

• The various machine learning problems and datasets
• How to evaluate the performance of a predictive model
• Machine learning algorithms
• Python environment setup and the required libraries