Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples

tl;dr This is a beast of a book. Definitely recommend to have as a permanent reference when working in interpretable machine learning.I have found this to be insightful (although I still have halfway to go). For beginners, this will be a great introduction and reference -- conventions, terms and code examples are thorough and well explained (which is probably why the book is lengthy). For intermediates and more advanced folk this is perfect, there are enough gold nuggets of information spread throughout the book that it will become a great resource for future reference. It feels like the book covers the majority of (if not all of the) topics needed to tackle interpretable machine learning today. In most books I’ve read, whether coding cookbooks or theoretical ones, the number of examples provided are few, but in this book, they are abundant. Also I would get the ebook, unless you prefer a hardcopy.

I’m a computational neuroscientist in training, and in this field (and in related fields) we always try to find biologically plausible models. While this book does not delve into what mother nature does, it provides a beautiful catalog of methods and explanations for how to apply state of the art machine learning techniques and what they actually might mean when used. Importantly, it provides post-hoc methods to explain what many others have taken for granted with today’s easy to use, out of the box machine learning techniques. I’ll be using this as a reference for many of my future projects.

As models are getting more complex day by day it is becoming difficult to interpret them. This book helps by addressing the exact issue. The book is well thought out.

I usually go on reddit and do heavy research before buying a book (there are so many!!). This time I took a gamble on this book after encountering it on linkedin. I was not disappointed!! I’ve been trying to enter the machine learning field as a novice and wasn’t sure how to start but this book not only goes through detailed examples, it goes through big picture ideas, ideas that we have to be mindful of as machine learning, and deep learning for that matter, continues to encompass our every day. Definitely recommend!

Because this book is getting a lot of attention I decided to buy it. Ok, full disclosure, not an expert in this field, but have been trying to keep up with tech with leisure reading for principles and ideas I can apply in my field. The book is technical, it’s not a walk in the park, but even with my basic statistics I was able to follow a lot of it. Very rich with examples and would recommend it for other people like me trying to get their feet wet.

It is an excellent book that integrates the fundamentals of ML evaluation metrics, with the elements to interpret them. This book also exposes with examples and python code how to evaluate and interpret these metrics. This book also makes a valuable contribution to the understanding and taxonomy of bias in ML.

Great resource for understanding interpretable ML for self-learners.

Interpretable Machine Learning with Python is a comprehensive guide that provides a detailed dive into the key aspects and challenges of ML interpretability. The book is a treasure trove of knowledge for anyone looking to build fairer, safer, and more reliable models. Any analytics practitioner knows that it's easy to find decent resources on applying black-box models, but there are vastly fewer resources for ML interpretability. This one is no doubt canonical for anyone looking for greater ML explainability and building more stable models.The author begins with the fundamentals of interpretability, its relevance in business, and its key aspects and challenges. This sets the stage for a thorough exploration of white-box, black-box, and glass-box models, along with their trade-offs. The book then delves into a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text.What sets this book apart is its practical approach. The author provides step-by-step code and examples that help readers interpret model outcomes. The book also guides readers on how to tune models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods explored range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. I'm confident that models I deploy will now better meet the needs of business stakeholders I support.By the end of this book, I've not only understood ML models better but also am equipped to enhance them through interpretability tuning. This book is a must-read for data scientists, ML developers, and data stewards who are under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners looking for a digestible intro for this topic.In short, Serg's work is a valuable addition to the field of machine learning, providing readers with the tools and knowledge they need to navigate the complex landscape of ML interpretability. I highly recommend it!