Interpretable Machine Learning: Explainable Regression Models with H2O

As machine learning models become more sophisticated, understanding why a model makes a specific prediction is just as important as the prediction itself. This text-based course guides you through the core principles of explainable artificial intelligence (XAI) using the powerful H2O framework. You will transition from treating machine learning as a black box to confidently explaining regression predictions to stakeholders. Through clear written explanations, structured code walkthroughs, and conceptual exercises, you will learn how to extract, analyze, and communicate the underlying drivers of your regression models. What you'll learn: Understand the fundamental concepts of model interpretability and explainable AI; Calculate and interpret global variable importance to identify key drivers in your regression models; Apply Shapley values to explain individual local predictions with mathematical rigor; Perform detailed residual analysis to diagnose model weaknesses and uncover systematic bias; Configure H2O explainability tools to generate clear, structured model explanations; Implement modern model governance practices to ensure fairness and transparency in your workflows. The course starts with foundational definitions of interpretability before moving into practical code implementations using H2O. You will progress from global model inspection to local prediction explanations, wrapping up with modern evaluation and bias detection techniques. This course is designed for aspiring data scientists, analysts, and developers who are new to model interpretability. No prior experience with H2O is required, though a basic familiarity with programming concepts is helpful. Start reading today to make your regression models transparent, reliable, and easy to explain.