Embarking on a journey into machine learning and data science can feel like stepping into a vast ocean of information. Like any worthwhile expedition, having the right maps and guides is crucial. In the world of machine learning, these guides come in the form of books. For years, I’ve navigated this field by consistently seeking out books that challenge my understanding and expand my knowledge. These books have been the bedrock of my machine learning expertise, resources I continually revisit and recommend.
If you’re serious about learning machine learning or data science, investing your time in reading the right books is invaluable. When I approach a new topic, my first step is always to find a highly-regarded book and immerse myself in it from cover to cover. This deep dive allows me to build a solid foundational understanding, and then I revisit specific sections as needed to reinforce key concepts. The books listed below are packed with such memorable and impactful information.
Arranged in a roughly ascending order of complexity, this list is designed to guide you no matter your starting point. If you’re completely new to machine learning or data science, begin with the first recommendation. If you already possess a solid foundation in Python and mathematics, feel free to jump to the later suggestions.
This compilation isn’t exhaustive, and many excellent books are out there. If you believe a particular book deserves a place on this list, please share it in the comments below to benefit fellow learners.
Machine Learning for Humans by Vishal Maini and Samer Sabri
Alt text: “Machine Learning for Humans book cover, a beginner-friendly guide to understanding machine learning concepts.”
Born from a popular Medium series, “Machine Learning for Humans” is crafted to demystify machine learning. Vishal Maini and Samer Sabri expertly translate complex ideas into an accessible and engaging format.
For those seeking a comprehensive, entry-level resource to grasp fundamental machine learning concepts without prior experience, this book is an ideal starting point. Even seasoned machine learning practitioners can benefit from its clear explanations and find inspiration for communicating their work to a broader audience.
“Machine Learning for Humans” excels at making intricate topics understandable, serving as a fantastic launchpad for anyone beginning their machine learning journey.
Python for Data Analysis by Wes McKinney
Alt text: “Python for Data Analysis book cover, essential guide to mastering Pandas for data manipulation in machine learning.”
If you’re venturing into data science or machine learning, you’ll quickly encounter Pandas, a powerful Python library for data analysis. “Python for Data Analysis” is an indispensable resource, authored by Wes McKinney, the very creator of Pandas. Learning directly from the source ensures you’re gaining insights from the highest authority.
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 provides comprehensive guidance on leveraging Pandas for data analysis, cleaning, transformation, and ultimately, for effective data science and machine learning workflows.
No matter your level of experience as a data scientist or machine learning professional, deepening your Pandas proficiency is always a worthwhile endeavor. “Python for Data Analysis” is the definitive guide to achieving mastery.
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
Alt text: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow book cover, a practical guide for applied machine learning.”
For those diving into machine learning and seeking a comprehensive, hands-on resource, “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” is your ultimate guide. Aurélien Géron expertly leads you through two essential machine learning libraries, Scikit-Learn and TensorFlow (now updated to include Keras), teaching core machine learning concepts through practical, coded examples.
The book’s strength lies in its practical approach. Each concept is accompanied by corresponding code, enabling you to not only understand the capabilities of machine learning but also to adapt and apply the provided examples to your own projects and challenges. This book bridges the gap between theory and practice, making machine learning tangible and actionable.
The latest edition expands its coverage to include Keras, a highly popular deep learning framework, making it even more comprehensive and relevant for today’s machine learning landscape. Whether you’re a beginner or looking to solidify your practical skills, this book is an invaluable asset.
Grokking Deep Learning by Andrew Trask
Alt text: “Grokking Deep Learning book cover by Andrew Trask, learn deep learning from scratch with Python and NumPy.”
My deep learning journey began with Udacity’s Deep Learning Nanodegree, where Andrew Trask was one of the instructors. Now a researcher at DeepMind, Trask has a remarkable ability to explain complex topics.
Even in its early stages, with only a few chapters available, “Grokking Deep Learning” captivated me. I remember sitting on my couch, absorbed in each page, learning to build neural networks from the ground up using NumPy, Python’s numerical powerhouse.
Trask’s talent for descriptive analogies made intricate machine learning concepts remarkably clear. Phrases like “Deep learning hyperparameters can be tuned like the dials on your oven” made abstract ideas relatable and understandable. I eagerly awaited each new chapter.
Now, the complete book is available, offering a unique opportunity to learn deep learning from the ground up, guided by hands-on examples from a leading practitioner in the field. “Grokking Deep Learning” is more than just a textbook; it’s a deeply engaging learning experience.
The 100-Page Machine Learning Book by Andriy Burkov
Alt text: “The Hundred-Page Machine Learning Book cover, a concise and comprehensive overview of machine learning fundamentals.”
“The 100-Page Machine Learning Book” is aptly named – it’s a powerhouse of machine learning knowledge distilled into a remarkably concise format. As I mentioned in my book review, it’s the perfect “start here and continue here” resource for machine learning. Following “Machine Learning for Humans,” if you’re eager to delve deeper into the mechanics of machine learning algorithms, this book is an excellent next step.
What sets this book apart is its problem-solution approach. It not only identifies common challenges in machine learning but also provides effective solutions, along with clear explanations of the reasoning behind them—all within just 100 pages.
While you could indeed read it in a single day, the value lies in taking your time to absorb the dense information. Learning machine learning, like any complex field, requires time and careful consideration. For those seeking further exploration, QR codes throughout the book link to curated extra-curricular materials by the author, Andriy Burkov, enhancing the learning experience 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
Alt text: “Deep Learning Book cover by Goodfellow, Bengio, and Courville, the definitive guide for advanced deep learning study.”
“Deep Learning,” often referred to as the “Deep Learning Bible,” is the newest addition to my collection, and I opted for the hard copy edition. This book truly embodies the criteria I mentioned at the beginning—a book that continually challenges my understanding.
I’m particularly excited to delve into the foundational math sections. My learning journey has been primarily code-first, which is reflected in the order of books on this list. However, the essence of deep learning and machine learning lies 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 titans of the deep learning world—Ian Goodfellow (inventor of GANs), Yoshua Bengio (a pioneer of deep learning), and Aaron Courville (whose academic works have been cited nearly 50,000 times)—this book provides an in-depth exploration of all essential deep learning concepts.
“Deep Learning” is not just comprehensive; it’s a profound resource for anyone serious about mastering the theoretical underpinnings of deep learning.
Remember, machine learning is an expansive field. Use these books as a solid foundation upon which to build your knowledge. Crucially, complement your reading with practical application. Knowledge without application is of limited value. The most effective way to learn is often through hands-on experience and, inevitably, by making mistakes.
If I’ve overlooked any essential books, please contribute your suggestions in the comments below to help other learners discover more valuable resources.
Keep learning and keep building.
PS: For a different perspective, there’s 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.
