Embarking on a journey into machine learning and data science requires a solid foundation. While online courses and tutorials are valuable, books offer a depth and structured approach that is irreplaceable. For anyone serious about mastering machine learning, investing time in reading the right books is crucial. I’ve personally built my machine learning knowledge upon a curated selection of books, constantly revisiting them to reinforce and expand my understanding. If you’re looking to delve into this exciting field, the following books are invaluable resources worth your time.
When I approach a new subject like machine learning, my strategy always begins with finding a highly recommended book and thoroughly reading it from cover to cover. After the initial read, I revisit sections that particularly resonate or challenge me. The books listed below are filled with such impactful sections that have significantly shaped my learning path.
The books are arranged roughly in order of accessibility. If you’re new to machine learning or data science, I suggest starting from the top of the list. However, if you already have a grasp of Python and mathematical concepts, you might find it beneficial to begin with the books towards the bottom.
This list is curated based on my personal experience and is by no means exhaustive. The field of machine learning is vast and constantly evolving, and many excellent resources are available. If you believe another book deserves to be included, please share your recommendation in the comments below to benefit other learners.
Machine Learning for Humans by Vishal Maini and Samer Sabri
Originally a series on Medium, “Machine Learning for Humans” was born from the authors’ desire to demystify machine learning concepts in an accessible and engaging way. They have truly succeeded in this mission.
If you’re seeking a starting point to grasp the fundamental concepts of machine learning without prior experience, this book is an excellent choice. It provides a zero-to-one resource that builds your understanding of crucial machine learning ideas. Even seasoned machine learning practitioners can benefit from this book, finding inspiration in its approachable style for communicating complex topics to a wider audience.
You can access it for free online, making it a highly accessible entry point into the world of machine learning.
Python for Data Analysis by Wes McKinney
When you begin your journey in data science or machine learning, you’ll quickly encounter Pandas, a powerful Python library for data analysis. What sets “Python for Data Analysis” apart is that it’s authored by Wes McKinney himself, the creator of Pandas. Learning directly from the source guarantees you’re mastering best practices and gaining profound insights.
As a machine learning engineer, a significant portion of my work involves using Pandas to preprocess and manipulate data, preparing it for machine learning models. This book comprehensively teaches you how to leverage Pandas for data analysis, cleaning, transformation, and ultimately, for effective data science and machine learning workflows.
Whether you are a data scientist or a machine learning engineer, a deep understanding of Pandas is indispensable. This book is an investment in your fundamental data manipulation skills.
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
If you’re diving into machine learning and need a practical, all-encompassing resource, “Hands-On Machine Learning” is an exceptional choice. This book guides you through two of the most influential machine learning libraries: Scikit-Learn and TensorFlow (now updated to include Keras as well). It emphasizes learning machine learning concepts through practical, coded examples.
Each concept discussed is accompanied by readily applicable code, allowing you to solidify your understanding by doing. You can read through the book to grasp the breadth of machine learning capabilities and then adapt the provided code examples to tackle your own projects and problems.
The latest edition expands its coverage to include Keras, a user-friendly deep learning framework, making it even more comprehensive and relevant for contemporary machine learning practice. It’s a highly recommended resource for anyone seeking a hands-on approach.
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. Now a researcher at DeepMind, Trask has a knack for explaining complex topics.
Even in its early access, with only a few chapters available, “Grokking Deep Learning” captivated me. I remember sitting and absorbing every page, learning how to construct a neural network from scratch using NumPy, Python’s numerical powerhouse.
Trask’s descriptive analogies are particularly effective in making machine learning concepts relatable. Phrases like, “Deep learning hyperparameters can be tuned like the dials on your oven,” made abstract ideas concrete and understandable.
Now, the complete book is available, offering a comprehensive journey into deep learning from the ground up. It’s an opportunity to learn from one of the field’s most gifted educators, complete with hands-on examples that reinforce learning.
The Hundred-Page Machine Learning Book by Andriy Burkov
I’ve previously described “The Hundred-Page Machine Learning Book” as both a starting point and a continuing reference for machine learning. After gaining foundational knowledge from “Machine Learning for Humans”, if you are eager to delve deeper into the mechanics of machine learning algorithms, this book is an ideal next step.
What I appreciate most is its concise yet comprehensive approach. It tackles common problems in machine learning, provides effective solutions, and explains the reasoning behind those solutions – all within a mere 100 pages.
While you could technically read it in a day, I recommend taking your time. Mastering machine learning, like any complex subject, requires time and thoughtful consideration.
For those seeking further exploration, the book includes QR codes throughout, linking to additional curated content by the author, expanding your learning beyond the pages.
Read for free on the book’s website.
Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
“Deep Learning” is the latest addition to my personal library, and I opted for the hard copy edition. This is the book that truly fits the “too hard to read” criterion I mentioned at the beginning – in the best possible way.
I’m particularly excited about the in-depth mathematical sections presented at the beginning. My learning journey has been primarily code-first, which is reflected in the order of books I’ve recommended. However, the core of deep learning and machine learning is grounded in applied mathematics. While coding frameworks evolve, the underlying mathematical principles remain constant. Linear algebra, for instance, will always be linear algebra.
Authored by three luminaries in the deep learning domain – Ian Goodfellow (inventor of GANs), Yoshua Bengio (a pioneer of deep learning), and Aaron Courville (whose academic work boasts nearly 50,000 citations) – this book delves into the essential deep learning concepts with unparalleled depth.
Remember, machine learning is an expansive field. Utilize these books as a robust foundation to build upon. Supplement your reading with practical application and hands-on projects. Applied knowledge is far more valuable than theoretical knowledge alone. The best learning often arises from making mistakes and actively engaging with the material.
If I’ve overlooked any essential books, please feel free to mention them in the comments below to help fellow learners discover more resources.
Keep learning and exploring the fascinating world of machine learning.
PS: There’s 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’s a work in progress, but definitely worth checking out.
