This section serves as a theoretical introduction to the Bayesian approach which will later help us understand the Naïve Bayes machine learning classification algorithm. We start with an intuitive example which Thomas Bayes himself introduced. Then, we dive into the mathematics behind his approach and derive Bayes’ theorem. Finally, we apply this theorem to classify an e-mail message as a spam or not-spam (also known as a ham).

In this section you will learn how to install all Python packages relevant for the next part of the course focused on practice.

This is the practical section of the course where we roll our sleeves up and build our very own classification model. We use a dataset containing YouTube comments – some well-intended and others harmful. Our task is to train a model that could later serve as a spam comment detector. To do this, we make use of Python’s scikit-learn library, where a Naïve Bayes algorithm is implemented. Throughout this section, we will study the inner workings of the algorithm and learn how to interpret performance metrics such as accuracy, precision, recall, and F1 score.