Introduction to Conditional Probability and Bayesian Networks in Python

Probability is the foundation of modern data science, machine learning, and decision-making under uncertainty. To build reliable predictive models or analyze complex systems, you must first master how variables interact and influence one another. This course guides you from the fundamental rules of chance to modeling complex dependencies with confidence. You will start by mastering key terminology, basic probability rules, and foundational definitions before moving into practical applications. Through clear, written explanations and structured code examples, you will learn how to calculate conditional probabilities and represent real-world scenarios using belief networks. What you'll learn: Understand the core concepts of joint, marginal, and conditional probability; Apply Bayes' theorem to update beliefs based on new evidence; Structure complex probability relationships using directed acyclic graphs and Bayesian networks; Implement probability models in Python using modern libraries; Analyze real-world scenarios to make calculated, data-driven decisions. The course begins with foundational probability theory, transitions into the mechanics of Bayes' theorem, and concludes with hands-on Python implementations for building and querying Bayesian networks. This course is designed for beginners, data enthusiasts, and aspiring analysts who want to build a strong mathematical foundation. No prior background in probability theory is required, though a basic familiarity with Python variables and loops will help you get the most out of the practical sections. Read the material, follow the structured text examples, and start modeling uncertainty today.