Probabilistic Machine Learning: Build a Naive Bayes Classifier

Understanding how machines make decisions under uncertainty is fundamental to modern artificial intelligence. Probabilistic classifiers allow us to predict outcomes while measuring the confidence of those predictions. In this written course, you will transition from basic probability theory to constructing a working binary classifier. You will learn the mechanics of Bayes' Theorem, explore how classical probabilistic models lay the groundwork for hybrid quantum-classical computing, and write clean, type-hinted Python code to predict outcomes from structured real-world data. What you'll learn: 1. Learn the foundational mathematics of probability, conditional probability, and Bayes' Theorem. 2. Understand the "naive" assumption in Naive Bayes and when to apply this classifier. 3. Implement a binary classification pipeline from scratch using modern Python type hints and clean code standards. 4. Prepare and preprocess structured datasets for probabilistic modeling. 5. Evaluate classifier performance using precision, recall, and modern metric analysis. 6. Explore how classical probabilistic models connect to emerging quantum-classical hybrid machine learning paradigms. The course begins with essential probability definitions and the mechanics of Bayes' Theorem before guiding you through data preparation and step-by-step model implementation. You will then learn how to evaluate your classifier and understand its role in modern advanced computing. This course is designed for aspiring data scientists, programmers, and AI enthusiasts who want a solid mathematical and practical foundation in probabilistic modeling. No prior machine learning experience is required, though basic familiarity with Python is helpful. Start reading today to build your understanding of probabilistic machine learning from the ground up.