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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

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Machine Learning for Algorithmic Trading

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Total £ 125.97

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Amazon verified reviews

Milin desai Apr 15, 2021

I've been reading this book from last over 2 weeks, and the way Author presented the concept are really interesting, and even if you don't have any technical background still you will be able to interpret and understand all the concepts. I should say I've learned more from this book than the 3 hours college classes at the university.if you are someone who is interested learning about machine learning focusing with python. This book is perfect you and for all levels, I was some where between the beginner and intermediate level but found this book really helpful.Thank you Serg for writing this wonder piece of tech art.

Amazon Verified review

N. J. Cruz Jun 19, 2021

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.

TF Jun 01, 2021

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.

conanlover6 Aug 16, 2021

This book goes into great depth and helps a lot to understand what interpretability is. Recommended for those new to the field as well as veterans.

Aren Carpenter Oct 02, 2021

This book is the perfect next step for data science intermediates or motivated beginners to get hands on experience with real-world scenarios. It is approachable and Serg explains complicated topics deftly. I highly recommend making it your next ML book.

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

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