In this chapter, you will learn about two popular classification machine learning algorithms: the Naive Bayes algorithm and the linear support vector machine. The Naive Bayes algorithm is a probabilistic model that predicts classes and categories, while the linear support vector machine uses a linear decision boundary to predict classes and categories.
In this chapter, you will learn about the following topics:
- The theoretical concept behind the Naive Bayes algorithm, explained in mathematical terms
- Implementing the Naive Bayes algorithm by using scikit-learn
- How the linear support vector machine algorithm works under the hood
- Graphically optimizing the hyperparameters of the linear support vector machines
