10 Free Must-Read Machine Learning E-Books for Aspiring Data Scientists and AI Engineers

Machine learning (ML) has exploded in popularity and importance in recent years as more and more companies leverage AI to drive innovation. The number of ML and data science job openings has grown by over 650% since 2012 and median salaries now top $120,000 per year (source).

If you‘re looking to break into this high-demand field, self-study is a great place to start – and luckily, there are plenty of free high-quality resources available online. Here are 10 of the best free e-books to start building your machine learning skillset, divided into 3 key categories:

Introductory/Foundational Books

New to data science and ML? These beginner-friendly books will help you grasp the core concepts:

1. Introduction to Machine Learning

Author: Ethem Alpaydın
Published: 2010
Pages: 400
Difficulty Level: Beginner

Alpaydın‘s book has become a go-to resource for those just starting out in ML.

Key topics covered:

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Neural networks
• Kernel machines
• Graphical models
• Bayesian learning
• Combining multiple learners

What makes it unique:

• Focuses on concepts and intuitions over math and equations
• Includes real-world examples and case studies
• Provides MATLAB code samples

According to popularity metrics on Google Scholar, this book has been cited over 10,000 times, making it one of the most popular introductory ML texts.

2. Understanding Machine Learning: From Theory to Algorithms

Authors: Shai Shalev-Shwartz and Shai Ben-David
Published: 2014
Pages: 397
Difficulty Level: Intermediate

For those looking to dive deeper into the theoretical foundations of machine learning, this book is an excellent resource. It connects core ML concepts to the underlying mathematical principles and assumptions.

Noteworthy features:

• Covers advanced topics like convex optimization, PAC learning, and stability
• Includes exercises and problems with each chapter for self-study
• Mathematically rigorous yet still accessible

Shai Shalev-Shwartz is a renowned ML researcher and winner of numerous awards, so you can trust the book‘s technical accuracy and relevance. It currently has a 4.7/5 star rating on Goodreads.

3. Dive into Deep Learning

Authors: Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
Published: 2020
Pages: 1020
Difficulty Level: Intermediate

This massive, comprehensive book focuses on the hot topic of deep learning, a powerful subset of ML techniques. It‘s available as an interactive web-based version with Jupyter notebooks for hands-on learning.

Key strengths:

• Expansive and in-depth coverage of deep learning methods and applications
• Clear explanations with minimal assumed knowledge
• Extensive code examples in multiple frameworks (PyTorch, TensorFlow, MXNet)

While fairly new, this book has quickly become a popular self-study resource, with over 7k stars and 2k forks on its GitHub repo.

Books on Specific ML Topics

Once you have a handle on the fundamentals, you can explore key areas of ML in more depth with these focused reads:

4. Machine Learning for Anomaly Detection

Author: Ira Kaplan and Mario Berger, PhD
Published: 2022
Pages: 110
Difficulty: Intermediate

Anomaly detection is a critical application of ML used to find patterns, outliers, and rare events in data for fraud prevention, medical diagnosis, predictive maintenance, and more. This practical book demonstrates how to design and build effective anomaly detection systems.

Why it‘s valuable:

• Includes detailed case studies across domains like healthcare, finance, IoT
• Compares different techniques (rule-based, ML, hybrid approaches)
• Discusses common challenges and best practices

Ira Kaplan and Mario Berger are ML engineers and managers at Fortune 100 companies, so their guidance comes from real-world industry experience. The book currently has a 4/5 star rating on O‘Reilly.

5. Probabilistic Machine Learning: An Introduction

Author: Kevin P. Murphy
Published: 2021
Pages: 1331
Difficulty: Advanced

For mathematically inclined readers looking for a rigorous study of ML techniques through a probabilistic lens, this book by leading ML researcher Kevin Murphy is a dense but rewarding read. It unifies many ML methods under a common probabilistic framework.

Standout features:

• Focuses on probabilistic methods for representing and reasoning about uncertainty
• Covers advanced topics like Bayesian inference, graphical models, deep learning
• Includes exercises and demos with each chapter

As Kevin Murphy notes in the preface:

"Machine learning is all about uncertainty…Probability theory gives us principled ways of representing and manipulating uncertainty, while statistics gives us techniques for quantifying uncertainty in the face of limited data."

Books Focused on Practical/Applied ML

Ready to get your hands dirty and start building ML models? These books provide practical guidance for applying ML techniques to real-world problems:

6. Hands-On Machine Learning with R

Author: Bradley Boehmke & Brandon Greenwell
Published: 2019
Pages: 488
Difficulty: Intermediate

If you prefer R over Python for data science and ML work, this book is for you. It provides a hands-on guide to the entire ML workflow in R.

Key topics:

• Data preprocessing
• Feature engineering
• Model training and evaluation
• Hyperparameter tuning
• Model deployment

Why it‘s great for R users:

• Uses popular R packages like caret, h2o, keras
• Demonstrates ML techniques from regression to deep learning
• Includes end-to-end case studies on real datasets

The corresponding GitHub repo has over 500 stars and provides all the code samples to follow along.

7. Machine Learning Yearning

Author: Andrew Ng
Published: 2018 (draft)
Difficulty: Intermediate

Renowned Stanford professor and Coursera co-founder Andrew Ng provides a practical guide to structuring machine learning projects in this free book draft. He shares techniques and advice for navigating tricky real-world ML problems.

Valuable insights on:

• Selecting performance metrics
• Debugging ML models
• Deciding what data to collect
• Handling mismatched training/test sets
• Error analysis and prioritization
• ML project management

As Andrew states in the introduction:

"This book will teach you how to align on ML strategies in a team setting…I am writing this book to share what I‘ve learned, to help you avoid some of the mistakes I‘ve made."

8. Interpretable Machine Learning

Author: Christoph Molnar
Published: 2019
Pages: 314
Difficulty: Intermediate

As ML models get more complex and high-stakes, it‘s critical that we understand how they arrive at decisions. This book focuses on techniques for interpreting what‘s happening inside the "black box."

Key interpretability topics:

• Feature importance
• Partial dependence plots
• Individual conditional expectation
• Local surrogate models
• Counterfactual explanations
• Adversarial attacks

Author Christoph Molnar is a statistician and ML researcher who has published extensively on interpretability. The corresponding GitHub repo has over 3k stars.

Keep Learning and Applying Your Knowledge

These 8 free e-books can give you an excellent foundation in both ML theory and practice – but they‘re really just a starting point. The field of AI and ML is rapidly evolving, so continuous learning is a must.

In addition to reading books, I‘d strongly encourage aspiring ML practitioners to:

• Take online courses and tutorials
• Work on projects to apply what you‘ve learned
• Read the latest ML research papers and blog posts
• Participate in ML competitions like Kaggle
• Attend conferences and workshops
• Connect with other ML enthusiasts through meetups or online communities

With dedication and plenty of hands-on practice, you can achieve your goal of becoming a machine learning expert. These free e-books will help kickstart your learning journey – pick one and start reading today!