Introduction to Graphs in Bayesian Networks with Python

Probabilistic graphical models are at the heart of modern artificial intelligence, but you cannot build them without a solid grasp of the underlying mathematics. This course bridges the gap between abstract graph theory and practical implementation, giving you the exact conceptual foundation needed to work with Bayesian networks. You will start with the fundamental terminology of directed acyclic graphs before moving on to complex network configurations. By reading through clear explanations and analyzing structured code snippets, you will learn how to represent, query, and reason about uncertainty using structured graphs. This text-based program ensures you master the essential math and programming patterns today's data practitioners use. What you'll learn: - Understand foundational graph theory terms including nodes, edges, parents, and children - Identify and analyze key network configurations such as chains, forks, and colliders - Apply the principles of d-separation to determine conditional independence in a network - Implement graph structures and probability distributions using modern Python libraries - Trace how information flows through a Bayesian network to make probabilistic inferences - Practice translating real-world dependency scenarios into structured directed acyclic graphs The course begins with essential definitions of graph structures and probability basics, ensuring you have a firm grasp of the core concepts. From there, you will explore how these structures govern independence assumptions and how to represent them programmatically in Python. This course is designed for beginners in probabilistic machine learning, data scientists looking to strengthen their theoretical foundations, and Python programmers curious about graphical models. No prior experience with Bayesian networks is required, though a basic familiarity with Python variables and functions is helpful. Start reading today to unlock the power of probabilistic graphical models in your data science workflow.