We have covered a lot of material in this chapter. Don't worry if you do not understand some of the mathematics presented here. The aim is to give you some intuition into how some common machine learning algorithms work, not to have a complete understanding of the theory behind these algorithms. After reading this chapter, you should have some understanding of the following:
- General approaches to machine learning, including knowing the difference between supervised and unsupervised methods, online and batch learning, and rule-based, as opposed to model-based, learning
- Some unsupervised methods and their applications, such as clustering and principle component analysis
- Types of classification problems, such as binary, multi-class, and multi-out classification
- Features and feature transformations
- The mechanics of linear regression and gradient descent
- An overview of...