As promised, in this chapter, we kick off our supervised learning journey with machine learning classification, specifically, binary classification. We will be learning with the goal of building a high-performing spam email detector. It is a good starting point to learn classification with a real-life example—our email service providers are already doing this for us, and so can we. We will be learning the fundamental concepts of classification, including what it does and its various types and applications, with a focus on solving spam detection using a simple yet powerful algorithm, Naïve Bayes. One last thing: we will be demonstrating how to fine-tune a model, which is an important skill for every data science or machine learning practitioner to learn.

We will get into detail on the following topics:

• What is machine learning classification?
• Types of classification
• Applications of text classification
• The Naïve Bayes classifier
• The mechanics of Naïve Bayes
• The Naïve Bayes implementations
• Spam email detection with Naïve Bayes
• Classification performance evaluation
• Cross-validation
• Tuning a classification model