About the Book: Interpretable Machine Learning is a practical guide to understanding why machine learning models make the predictions they do, rather than focusing solely on accuracy. Designed for experienced data scientists and ML engineers, it explores widely used explainable AI (XAI) and model interpretability techniques, explaining their strengths, limitations and practical applications. Readers will learn best practices, avoid common pitfalls and integrate explainability into their machine learning workflows to build more transparent, trustworthy and robust AI systems. With Python examples using popular frameworks and libraries including Keras, TensorFlow, PyTorch and Hugging Face, this book covers explainability for tabular, image and text-based models.