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.
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Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples Paperback – 26 March 2021
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About the Author
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making ― and machine learning interpretation helps bridge this gap more robustly.
- Language : English
- Paperback : 736 pages
- ISBN-10 : 180020390X
- ISBN-13 : 978-1800203907
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Amazon.com: 16 reviews
Keep as a permanent reference when working in interpretable machine learning.27 April 2021 - Published on Amazon.com
3 people found this helpful
N. J. Cruz
Great guide for those who look to apply Machine Learning tools, specially with Python20 June 2021 - Published on Amazon.com
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.
Great purchase21 June 2021 - Published on Amazon.com
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!
Excellent book for modern coders20 June 2021 - Published on Amazon.com
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.