This is a great book on Machine Learning, very clearly and logically laid out.

Fantastic book!

The topics are presented in a very logical order, and the content is rich and insightful. Step by step, it has helped me grow my understanding of machine learning.

Excellent, very well doumented

This book provides a clear, step-by-step guidance to machine learning with scikit-learn. It explains model building, preprocessing, and practical applications in a way that is easy to follow, making complex concepts approachable for beginners. A highly practical book for anyone looking to apply machine learning in Python.

Kevin Markham has a unique ability to take complex concepts and make them feel clear, manageable, and genuinely approachable. Throughout Master Machine Learning with scikit-learn, it’s obvious that he not only deeply understands the material, but also knows exactly how to communicate it effectively.

What sets this book apart is its clarity and efficiency. Kevin focuses on the concepts you actually need to learn first and presents them in a practical, easy-to-follow way. His teaching style makes the content accessible regardless of your background.

There are plenty of resources out there on similar topics, but finding someone who is both highly knowledgeable and an exceptional communicator is rare. Highly recommended for anyone looking to build a strong, confident foundation in machine learning.

The book is a practical, hands-on guide that excels at teaching readers how to build, evaluate, and improve machine-learning models using scikit-learn,it helped me strengthen my applied ML skills rather than just learn theory.

Kevin is a world-class teacher, and that translates just as well to this textbook. This is very much a practical guide to machine learning, and it really embodies the 80/20 principle: mastering the 20% of core concepts that can help you deliver 80% of the value. As a professor myself, this book captures the core foundations every one new to ML should learn and know.

Is this book going to turn you into a Senior Data Scientist at a FAANG company overnight? No. But will it make you competent and confident enough to start delivering real value on ML-related projects? Absolutely.

Highly recommended for anyone looking for a practical, approachable introduction to machine learning.

Wonderful hands-on introduction into ML with scikit learn! Gradual complexity increase, deep dives and comments regarding nuances, nice and thoroughly explained code snippets - all that forms a great foundation in the field

Whereas Geron's "Hands-On Machine Learning" (HOML) is a massive textbook that provides an overview of the ML landscape, Markham's "Master Machine Learning" (MML) is a much more focused, practical guide to scikit-learn that served my needs far better.

MML's strength is that it teaches you the best practices for effective Machine Learning AND how to implement every single one of those practices using scikit-learn.

The first part of MML shows you how to build a robust, pipeline-based workflow that includes the necessary data preparation steps and avoids the cardinal sin of data leakage. (This was the first book that explained data leakage in a way that "clicked" for me.)

The second part of MML teaches you how to evaluate and improve your pipeline by tuning the hyperparameters, trying out different models, ensembling models, using automated feature selection, and so on. (It's nice that HOML explains the properties of different models, but as MML points out, it's more valuable to build a reusable workflow in which you can quickly try out many different models than it is to specialize in just one type of model.)

The final part of MML gets into more advanced topics, such as feature engineering, high-cardinality categorical features, class imbalance, cost-sensitive learning, alternative evaluation metrics, and more. I haven't found this kind of depth in any other book.

Here's my overall recommendation:

If you're looking for an overview of the ML landscape, including unsupervised learning and deep learning, then HOML may be the book for you. But if you're specifically focused on supervised learning using scikit-learn and want to improve your skills quickly, then I haven't found a better guide than MML.