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Key benefits

Learn how to extract easy-to-understand insights from any machine learning model

Become well-versed with interpretability techniques to build fairer, safer, and more reliable models

Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models

Description

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with 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. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.

Who is this book for?

This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves 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 who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.

What you will learn

Recognize the importance of interpretability in business

Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes

Become well-versed in interpreting models with model-agnostic methods

Visualize how an image classifier works and what it learns

Understand how to mitigate the influence of bias in datasets

Discover how to make models more reliable with adversarial robustness

Use monotonic constraints to make fairer and safer models

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Frequently bought together

Interpretable Machine Learning with Python

$54.99

$52.99

Machine Learning for Algorithmic Trading

$57.99

Total $ 165.97

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Amazon verified reviews

M. Borel Apr 27, 2021

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.

Amazon Verified review

Alexandra Arteaga Jun 20, 2021

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!

Jaime Penser Jun 19, 2021

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.

Himanshu Jun 17, 2021

This book is great for python practitioners of Machine Learning. It has various code examples covering the majority of ML concepts. The book is written in such a way that it's is very easy to follow up (for all levels of Machine Learning practitioners - beginner/intermediate/advanced).It talks about basics concepts, advanced concepts, code snippets to follow through, it's a complete learning package.

Anthony Maddalone Apr 19, 2021

This book is a rare find. Too often the machine learning world is only focused on the building of ML models, with the interpretation really considered almost an after thought it seems. This book takes a different approach and teaches the reader of any level how to make sense of the "why's" and the "how's". I'm not even completely through the book, but I had to put down some thoughts. If you're new to the ML world, or even experienced this will be the best book purchase of 2021.. highly recommended!

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

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