Bayesian Networks with Python: Data Simulation and Training

How do you make reliable decisions when data is scarce or complex? Bayesian networks offer a powerful probabilistic framework to model uncertainties, especially in fields like healthcare and risk analysis. This text-based course guides you through the foundational concepts of probabilistic graphical models and teaches you how to implement them from scratch. You will start by understanding core probability theory, conditional independence, and directed acyclic graphs before moving on to practical coding. Through clear explanations and structured code walkthroughs, you will learn to generate simulated datasets and train models to analyze rare outcomes. What you will learn: Understand the foundational mathematics of Bayesian networks and conditional probability; Simulate structured datasets to mimic real-world scenarios with Python; Construct directed acyclic graphs to represent causal relationships; Train network parameters and structure using modern Python libraries; Perform inference to predict outcomes and support decision-making; Apply best practices for validating and testing your probabilistic models. This course begins with essential terminology and structural foundations, transitioning into hands-on simulation techniques and parameter estimation. It is designed specifically for beginners, data enthusiasts, and analysts looking to expand their predictive modeling toolkit. No advanced background in statistics or machine learning is required to succeed. Start reading today to master the fundamentals of probabilistic modeling.