Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

I like the approach the book takes; in each chapter a "mission" is stated and pursued, with text, Python code, and plots; the chapter concludes with a section on "mission accomplished", a summary, dataset sources, and a list of further reading. It is designed to be a starting point for beginners and something which can complement the knowledge of more advanced readers.The goal with Explainable AI is to "try to ensure safe, fair, and reliable models". Interpretability, the author states, is a very active area of research (most of which has not left the lab and is not yet widely used) and says that the book prioritizes the why over the how. The book covers a selection of interpretability tools. Black-box, white-box and glass-box models are mentioned.There are a lot of interesting examples, with the first being cardiovascular disease, which would probably be good for everyone to start studying in high school. I think this content is good to help all kinds of folks think about data and how to think about it when analyzing the world. It brings out a lot of details.In the last chapter, "What's Next for ML Interpretability?" the author states, "what matters most is to engage with the tools. Not using the interpretable machine learning toolkit is like flying a plane with very few instruments or none at all." The "further reading" list for that chapter includes articles on why many ML models don't hit the market and about AI projects failing.I agree that this book is a good starting point for beginners to consider and explore the issues, and they can do that with the text, the data and the code, and the Discord community, and by checking out the articles and books on the further reading lists, which all together are a very nice package.

This book is one of the finest literature to build scalable machine learning models with performance and accuracy in mind.This book starts with a basic definition of machine learning interpretation with an example of what a simple weight model, with codes and other dependenciesIt then flows into all the key definitions of the difference between interpretability and exploitability with business cases and examples.It follows with key concepts and current various challenges faced by major corporations in building the highly scalable model which starts from data dictionary data preparation and also predicting various methods and different algorithms and their scalable performance this is followed by various methodologies to solve interpretation which also brings to new topics between performance and interpretation then it dives into more advance topics like neural networks and NLP to solve for feature selection and mitigation methods and then closes on what are the future growth

This ambitious book takes on three huge tasks: surveying tools and philosophies to help developers better interpret and explain AI models and their outcomes, documenting real-world cases of bias being built into data sets before offering strategies showing how not to repeat those mistakes, and offering a broad and deep state of the art of AI algorithms, a fast-moving field to say the least. Appropriate for its scope it’s a huge book, but it’s also a living one, with code examples available and an active Discord community in support of the material. I recommend this book for AI practitioners of all skill levels, as well as for students who want to get ahead of what will be one of the most important challenges we face over the decades to come.

The book is accessible and not too technical, which makes it easy to follow. That said, the first three chapters felt basic and could have been skipped. The highlight for me was the coverage of Shapley values—it was clear, practical, and I'm now using it in a real project at work. Great resource for getting started with model interpretability.

I just finished reading "Interpretable Machine Learning with Python - Second Edition" Authored by Serg Masís and published by Packt.In the book, readers embark on a comprehensive journey through the intricate world of interpreting machine learning models. Authored with technical precision and practical insights, the book addresses the pressing need for understanding and explaining machine learning algorithms.The initial chapters lay a sturdy foundation, delineating the distinctions between interpretability and explainability while underscoring their significance in real-world applications. Through a compelling business case, readers grasp the imperative of interpretability in decision-making processes.Delving deeper, the book navigates through key concepts and challenges surrounding interpretation methodologies. From traditional model interpretations to the emergence of newer glass-box models, readers gain a nuanced understanding of interpretability paradigms.The narrative unfolds with an exploration of global and local model-agnostic interpretation methods, shedding light on feature importance and interactions. Anchors, counterfactual explanations, and visualization techniques offer multifaceted insights into model behaviors across various domains.The book extends its reach into the realms of convolutional neural networks (CNNs) and natural language processing (NLP) transformers, elucidating complex architectures through visualization and interpretation methods.Further chapters unravel the intricacies of multivariate forecasting, feature selection, bias mitigation, and causal inference methods, empowering readers to navigate through the interpretability landscape with finesse.Finally, discussions on model tuning, adversarial robustness, and future prospects in ML interpretability invite readers to contemplate the evolving role of transparency in machine learning systems."Interpretable Machine Learning with Python" emerges as an indispensable resource for practitioners, researchers, and enthusiasts alike, offering profound insights and actionable strategies to unravel the mysteries of machine learning models.