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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.
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.
This book is a great tool for anyone interested in Python development. It offers a unique approach to coding in Python that I have not experienced from any other reading material. The "Interpretability" approach it takes lends itself to those that have never coded in python before, but this unique take also caters to seasoned programmers and veterans. It is easy to read with lots of pictures and real world examples. I was particularly interested and impressed with the chapter on Visualizing Neural Networks, as that is an area that can be very confusing and hard to picture, but this book really helped me get a deeper understand of the content. I would say this is a must read for beginners in Machine Learning that are hoping to get a deeper, more clear picture of how many of the most intricate mechanisms of ML work.
I have been working in the field of machine learning for over two years, and being able to properly and comprehensibly interpret the ML models’ predictions and their contributed features have always been a challenge to me. Hence, I started reading this book with curiosity and high expectations. As much as I am only halfway through this book, I’d say I don’t regret putting the time into reading it: great visualizations and their interpretations, hands-on codes to demonstrating the concepts, and comprehensible language that’s suitable for both beginners and advanced ML practitioners. Moreover, though the title of the book is Interpretable Machine Learning with Python, we can always read this book to learn about the basics of Machine learning and Deep Learning. I would recommend this book if you either expect to gain hands-on experience in ML and DL or if you are seeking advancement in your career by improving your communication about your ML models with your stakeholders.
I received a free copy of this book in exchange for my honest review. Coming from an actuarial pricing background, the adoption of machine learning methods, however popular, has been limited for transparency and regulatory reasons. The need for interpretable models is paramount to reassure the regulators and the public that the ratemaking principles such as fairness, accountability and transparency in technical models are met. This is a recurrent theme in this book: how to make models “safe, fair and reliable”. I enjoyed reading this book for its simplicity and its ethical and practical approach. There are diverse use cases to go through with available Python codes. The mathematical properties behind these interpretable methods are not developed in this book. This is a hands-on approach that gives you the necessary tools WITH the insight on why and how to deploy these methods as a practitioner of machine learning models.
This book is a thorough, extensive look at to how to incorporate interpretable practices at every step in a machine learning project. The author encourages 3 layers of interpretability during a project’s lifecycle – fairness, accountability, and transparency – and then clearly explains why these factors matter, and how to achieve and measure each throughout. Each chapter is framed with a business problem, the machine learning process to solve it, and lots of code examples and visualizations to highlight both.
Each of the chapters also had something new for me to learn, as well as plenty of reminders on familiar concepts (I can never remember what LASSO stands for, so happy to have it included!). Great reference to have on hand, and a book I will be reaching for often.