"The widespread integration of artificial intelligence (AI) into various industries, propelled by recent advancements, has showcased machine learning systems achieving remarkable, even superhuman, performance across numerous tasks. However, this elevated performance often accompanies heightened model complexity, rendering these systems as ""black boxes."" The opacity surrounding their operations and decision-making processes raises concerns about understanding their inner workings."Explainable Artificial Intelligence (XAI)"" is a comprehensive guide concentrating on interpretability methods in machine learning. It offers a taxonomy of these methods along with links to their programming implementations, making it an invaluable reference for both theorists and practitioners. This book is tailored for researchers, data scientists, and professionals seeking insights into creating transparent and interpretable AI models, bridging the gap between theoretical understanding and practical implementation in the field of Explainable AI. "