In this chapter, we acquired the fundamental and important concepts of machine learning classification, including types of classification, classification performance evaluation, cross-validation, and model tuning, as well as learning about the simple yet powerful classifier, Naïve Bayes. We went in depth through the mechanics and implementations of Naïve Bayes with couple of examples and the most important one, the spam email detection project. In the end, we developed a high-performing spam detector with AUC score close to 1.

Binary classification is our main talking point of this chapter, and as you can imagine, multiclass classification will be that of the next chapter. Specifically, we will talk about support vector machines (SVMs) ...

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