best machine learning books in 2025
If you're looking to build a strong foundation in artificial intelligence, choosing the right machine learning books is essential. We've compiled a carefully selected list of the best machine learning books in 2025 that cover everything from fundamental concepts to practical techniques in data preparation, model building, and evaluation. Whether you're a beginner or a professional aiming to sharpen your skills, these ML books offer clear explanations and real-world examples that make complex topics accessible. Many of the featured titles also include downloadable machine learning book PDFs, so you can start learning right away from your device. Browse through our top picks and use the affiliate links to grab the best resources to advance your machine learning journey.
Designing Machine Learning Systems
An Iterative Process for Production-Ready Applications 1st Edition by Chip Huyen (Author)
Machine Learning System Design Interview
by Ali Aminian (Author), Alex Xu (Author)
The Hundred-Page Machine Learning Book
The Hundred-Page Books by Andriy Burkov (Author)
Why Machines Learn
The Elegant Math Behind Modern AI Hardcover – July 16, 2024 by Anil Ananthaswamy (Author)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron (Author)
Introduction to Machine Learning with Python
A Guide for Data Scientists 1st Edition by Andreas C. Müller (Author), Sarah Guido (Author)
Mathematics for Machine Learning
1st Edition by Marc Peter Deisenroth (Author)
Understanding Machine Learning
1st Edition by Shai Shalev-Shwartz (Author)
Machine Learning with PyTorch and Scikit-Learn
Develop machine learning and deep learning models with Python by Sebastian Raschka (Author), Yuxi (Hayden) Liu (Author), Vahid Mirjalili (Author)
Build a Large Language Model
From Scratch by Sebastian Raschka (Author)
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