Explainable AI and Machine Learning Model Interpretability

As artificial intelligence systems become more complex, understanding their decision-making process is critical for safety, compliance, and trust. This course guides you through the foundational principles and practical techniques of Explainable AI (XAI), helping you turn opaque algorithms into transparent systems across various architectures. What you will learn: Learn core terminology and mathematical foundations of model transparency. Understand global and local feature attribution techniques such as SHAP and LIME. Analyze visual and sequential models including CNNs, Transformers, and LLMs. Evaluate AI systems for bias, fairness, safety, and operational reliability. Practice inspecting model behavior using written code walkthroughs and structured explanations. The material starts with essential definitions and linear model mechanics before advancing to modern neural network diagnostics and LLM probing concepts. Designed for beginners in data science, analysts, and software developers, this text-based guide requires no prior background in explainability. Start reading today to build trustworthy and understandable AI solutions.