Bayesian Network Inference: Exact & Approximate Methods with Python

Bayesian networks are fundamental tools for modeling uncertainty and making informed decisions. This course introduces the core concepts behind these powerful graphical models, providing a pathway to understanding complex probabilistic relationships. By the end of this course, you will be able to construct, analyze, and perform inference on Bayesian networks using Python, gaining a foundational skill for data science and AI applications. What you'll learn: * Understand the foundational principles of Bayesian networks and conditional probability. * Apply exact inference algorithms, such as variable elimination, to compute precise probabilities. * Practice approximate inference methods, including sampling techniques, for complex networks. * Implement Bayesian network models and inference algorithms using Python. * Interpret the results of inference to derive insights and make probabilistic predictions. * Learn basic techniques for validating the performance and structure of your Bayesian models. The course begins with an introduction to probabilistic graphical models and Bayesian network structure, then progresses through the theory and practical implementation of various inference algorithms. You will explore both exact and approximate methods, culminating in hands-on application to solve real-world problems. This course is designed for beginners with a basic understanding of programming concepts and probability, who are looking to build a strong foundation in Bayesian networks and their practical application. Start your journey into the world of probabilistic modeling and unlock new ways to understand and predict with data.