For anyone venturing into the realm of machine learning, the journey can feel like navigating a vast and intricate landscape. One of the most effective ways to build a solid foundation is through reading. Throughout my own journey in machine learning and data science, I’ve consistently found that immersing myself in challenging books has been the cornerstone of my learning and growth.
The books listed below are ones that have pushed me, and in some cases, continue to push me to expand my understanding. They represent the bedrock upon which I’ve constructed my knowledge of machine learning and data science. I anticipate revisiting and rereading these for years to come, continually gleaning new insights.
If you are serious about learning machine learning or data science, investing your time in these books will be invaluable.
My approach to learning a new subject often involves identifying a highly-regarded book and reading it cover to cover. Following this initial read-through, I revisit and delve deeper into the concepts that particularly resonate or present challenges. These recommended books are rich with such impactful and thought-provoking content.
The books are presented in an order of increasing complexity, roughly based on approachability. If you are completely new to machine learning or data science, it’s advisable to start from the beginning of the list. However, if you already possess a solid grasp of Python and mathematical foundations, you might consider starting with the later recommendations.
This list is by no means exhaustive. There are undoubtedly many other excellent resources I haven’t included. Please feel free to share your suggestions in the comments below, so that others can benefit from a wider range of perspectives.
1. Machine Learning for Humans by Vishal Maini and Samer Sabri
This book originated as a series on Medium. The authors set out to demystify machine learning, aiming to explain its core principles in an accessible and engaging manner. They have succeeded admirably.
For those seeking a comprehensive introductory resource to build a fundamental understanding of key machine learning concepts, particularly if you are new to the field, “Machine Learning for Humans” is an excellent starting point. Even seasoned machine learning practitioners can benefit from reading this book. It serves as a source of inspiration for communicating complex technical ideas in a way that is easily understandable to a broader audience.
2. Python for Data Analysis by Wes McKinney
Embarking on a journey into data science or machine learning inevitably involves working with Pandas, a powerful Python library designed for data analysis. A significant advantage of this book is that it is authored by Wes McKinney, the very creator of Pandas. This ensures you are learning from the ultimate authority.
In my experience as a machine learning engineer, a substantial portion of my time is dedicated to using Pandas for data manipulation, preparing datasets for machine learning models.
This book provides a thorough guide on leveraging Pandas to effectively analyze, clean, and transform your data, specifically for data science and machine learning applications.
For any aspiring or practicing data scientist or machine learning professional, a deep understanding of Pandas is indispensable.
3. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
If you’re diving into machine learning and are looking for a comprehensive, practical resource, this book is highly recommended. It expertly guides you through two of the most influential machine learning libraries, Scikit-Learn and TensorFlow (and Keras in the 2nd edition), teaching you core machine learning concepts through hands-on, coded examples.
Each concept discussed is accompanied by corresponding code implementations. This practical approach allows you to not only grasp the theoretical underpinnings of machine learning but also to immediately apply them. You can read through the book to gain a broad understanding of machine learning capabilities and then adapt the provided code examples to address your own specific problems.
Note: The 2nd edition significantly expands on the original by including Keras, a widely adopted deep learning framework, making it even more relevant and comprehensive.
4. Grokking Deep Learning by Andrew Trask
My initial foray into deep learning was through Udacity’s Deep Learning Nanodegree, where Andrew Trask was one of the instructors. He is now a researcher at DeepMind, a leading AI research laboratory.
Even in its early stages, with only a few chapters released, “Grokking Deep Learning” captivated me. I remember sitting and meticulously going through each page, learning how to construct a neural network from scratch using NumPy, Python’s fundamental numerical library.
I was immediately drawn to Andrew’s effective use of descriptive analogies to explain complex machine learning concepts.
His analogy, “Deep learning hyperparameters can be tuned like the dials on your oven,” perfectly illustrates the intuitive yet crucial process of hyperparameter tuning.
I eagerly anticipated and devoured each new chapter as it became available.
Now, the complete book is published and readily accessible.
“Grokking Deep Learning” offers a unique opportunity to learn deep learning from the ground up, guided by hands-on examples and insights from a leading practitioner in the field.
5. The 100-Page Machine Learning Book by Andriy Burkov
“The 100-Page Machine Learning Book” is an excellent resource to “start here and continue here” in machine learning. As I described in my book review, after gaining foundational knowledge from “Machine Learning for Humans,” this book is ideal if you are eager to delve deeper into the mechanics of machine learning algorithms.
What I particularly appreciate is its concise yet comprehensive approach. It covers common problems encountered in machine learning, provides practical solutions, and explains the reasoning behind those solutions—all within just 100 pages.
It is possible to read this book in a single day, but taking your time is recommended. Learning any new subject, especially machine learning, requires time for absorption and reflection.
For those who wish to explore further, the book includes QR codes throughout, linking to additional curated content selected by the author.
6. Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
This book is the most recent addition to my personal collection, and I opted for the hard copy edition. It perfectly embodies the criteria I mentioned at the beginning of this article – a book that challenges me to grow.
I am particularly enthusiastic about the mathematical sections at the beginning. My learning journey has been primarily code-first, which explains the order of books in this list. However, deep learning and machine learning are fundamentally rooted in applied mathematics. While coding frameworks may evolve, the underlying mathematical principles remain constant. Linear algebra, for example, will always be linear algebra.
“Deep Learning” is authored by three giants in the deep learning field. Ian Goodfellow is renowned as the inventor of GANs (Generative Adversarial Networks), Yoshua Bengio is recognized as one of the pioneers of deep learning, and Aaron Courville’s academic contributions have been cited nearly 50,000 times.
This book provides an in-depth exploration of all the essential deep learning concepts you need to understand.
Remember, machine learning is a vast and continually expanding field. Use these books as a strong foundation to build your knowledge upon, and actively enhance your understanding through practical application.
Knowledge without application is of limited value. The most effective learning often comes from making mistakes and learning from them.
If I’ve overlooked any other essential books, please feel free to share your recommendations in the comments below, benefiting the community.
Keep learning and exploring!
PS: There is also a video version of this article on YouTube which includes a bonus book recommendation, The Mechanics of Machine Learning by Jeremy Howard and Terence Parr. It is still a work in progress but worth checking out.
