Interpretable Machine Learning with Python
This book dives deep into the essence of making complex machine learning models understandable and accountable. This book covers everything from white-box models like linear regression and decision trees to a comprehensive suite of model-agnostic methods for black-box models. Techniques such as SHAP, LIME, and counterfactuals are explored in depth, alongside methods for understanding deep learning models for vision and text.
It lays out advanced techniques, including causal inference and quantifying uncertainty, making it an essential resource for data scientists, machine learning engineers, MLOps engineers, and anyone vested in AI's responsible development and deployment. It's also a good resource for those beginning their journey in machine learning, provided they have a solid foundation in Python.
This book is more than just a guide—it's a bridge to understanding the intricate dance between artificial intelligence and the real world, ensuring that the technology we build today is both understandable and ethical for tomorrow's challenges.
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Testimonials
Serg is one of those authors who brings true passion to their work. To the best of my knowledge, his new book, Interpretable Machine Learning with Python, Second Edition offers the most systematic, clear, and comprehensive coverage of explainability and interpretability methods in Python available on the market. Even if you’re a seasoned practitioner, you’ll likely learn something new from this book.
Each of the chapters also had something new for me to learn, as well as plenty of reminders on familiar concepts... Great reference to have on hand, and a book I will be reaching for often.
This book dives deep into these fundamental concepts that need to be demystified for beginners and advanced specialists. Serg Masís takes the time to help the reader understand the difference between interpretability and explainability. By the end of the book, you will be able to face the challenges of real-life AI implementations that require interpretability for legal reasons and to gain user trust.
This book changed how I look at machine learning. I just finished it. Worth every second. This is for anyone who wants to build real-world machine learning applications. It's practical and to the point. Interpretability 101.