Interpretable Machine Learning: Building Transparent AI Models is your essential guide to making sense of the black box. As AI systems become increasingly powerful and complex, the demand for transparency, accountability, and trust in machine learning models has never been more critical.
This eBook explores the core principles, techniques, and tools used to make machine learning models interpretable and explainable. From SHAP and LIME to rule-based models and visualization methods, you’ll learn how to design systems that not only perform but also justify their predictions.
Ideal for data scientists, machine learning practitioners, and decision-makers, this book empowers you to bridge the gap between accuracy and interpretability — making AI both effective and ethically responsible
