Interpretable Machine Learning with Python

Interpretable Machine Learning with Python is your comprehensive guide to making machine learning models understandable and reliable. With step-by-step examples and practical insights, this book introduces interpretability techniques and real-world applications to help you make models that are not only high-performing but also ethical and safer to use.

What this Book will help me do

• Understand the importance of model interpretability and its implications in various industries.
• Gain proficiency in applying model-agnostic and intrinsic interpretation techniques.
• Learn to visualize deep learning model behaviors like convolutional neural networks (CNNs).
• Acquire tools to detect and mitigate biases within datasets effectively.
• Develop skills to implement reliability-enhancing model modifications like adversarial robustness and monotonic constraints.

Author(s)

Serg Masís brings extensive expertise as a seasoned data scientist with a passion for making complex topics accessible. Through a career spanning diverse data challenges, Serg specializes in delivering insights into machine learning interpretability. With this book, he aims to empower readers with essential skills for creating transparent and reliable AI systems.

Who is it for?

This book is ideal for professional data scientists, machine learning engineers, and data stewards aiming to understand and improve model interpretability in their work. It is also suitable for self-learners and beginners exploring this field, provided they have foundational knowledge of Python programming and basic machine learning concepts.