Are you eager to dive into the world of machine learning but feel overwhelmed by the sheer volume of resources? Choosing the right Machine Learning Book can be a pivotal step in your learning journey. Like needing a reliable map for uncharted territory, a well-chosen book can illuminate complex concepts and guide you from novice to practitioner.
Throughout my journey in machine learning and data science, I’ve consistently relied on books to build a robust foundation. The books listed below aren’t just theoretical texts; they are the cornerstones of my understanding, resources I revisit and recommend time and again. Whether you’re taking your first steps or seeking to deepen your expertise, these books are invaluable investments of your time.
My approach to learning a new subject typically begins with identifying a highly-regarded book and immersing myself in it from cover to cover. This initial deep dive allows me to grasp the core principles. Then, I revisit specific sections that resonate or present challenges, solidifying my understanding. The books I’m sharing here are filled with such impactful sections that have significantly shaped my knowledge and career.
To help you navigate this list, I’ve organized the books in order of approachability. If you’re completely new to machine learning, start with the first recommendation. If you already possess a solid foundation in Python and mathematics, feel free to jump to the later, more advanced titles.
This list, while comprehensive, is by no means exhaustive. The field of machine learning is vast and ever-evolving. If you believe there’s a must-read machine learning book that deserves inclusion, please share it in the comments below to benefit fellow learners!
1. Machine Learning for Humans by Vishal Maini and Samer Sabri
Alt text: “Machine Learning for Humans book cover, showcasing a friendly and approachable design, ideal for beginners in machine learning.”
Originally a popular series on Medium, Machine Learning for Humans is crafted to demystify machine learning. Vishal Maini and Samer Sabri excel at explaining intricate concepts in a way that’s both readable and engaging.
If you’re searching for an entry-level resource to build a fundamental understanding of crucial machine learning concepts, and you’re new to the field, look no further. This book is perfectly tailored for beginners. Even seasoned machine learning practitioners can benefit from its accessible approach, gaining inspiration on how to communicate complex ideas to a broader audience.
2. Python for Data Analysis by Wes McKinney
Alt text: “Python for Data Analysis book cover, featuring the author Wes McKinney, the creator of Pandas, a key library for machine learning data manipulation.”
Embarking on data science or machine learning inevitably involves using Pandas, the powerful Python library for data analysis. Python for Data Analysis is the definitive guide, authored by Wes McKinney himself, the 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 is an essential resource for mastering these skills.
Inside, you’ll discover how to leverage Pandas to effectively analyze, clean, transform, and utilize your data for both data science and machine learning applications. A deep understanding of Pandas is always an asset for any aspiring data scientist or machine learning professional.
3. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
Alt text: “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow book cover, highlighting practical applications of popular machine learning frameworks.”
For those seeking a comprehensive, hands-on resource to enter the field of machine learning, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow is an excellent choice. This book guides you through Scikit-Learn, Keras, and TensorFlow, three of the most impactful machine learning libraries. It emphasizes practical learning, teaching machine learning concepts through well-structured, coded examples.
Every concept discussed is reinforced with corresponding code, enabling you to immediately apply what you learn. You can gain a solid understanding of machine learning capabilities and then adapt the provided code examples to tackle your own projects and problems. This practical approach makes it one of the best machine learning books for applied learning.
Pre-order latest edition on Amazon.
4. Grokking Deep Learning by Andrew Trask
Alt text: “Grokking Deep Learning book cover, featuring a visually engaging design that reflects the deep dive into neural networks and deep learning concepts within.”
My personal 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 knack for making complex topics understandable.
Even when only a few chapters of Grokking Deep Learning were available, I was captivated. I spent hours absorbing each page, learning to build neural networks from scratch using NumPy.
Trask’s descriptive analogies are particularly effective in explaining machine learning concepts, making abstract ideas concrete and relatable. His analogy, “Deep learning hyperparameters can be tuned like the dials on your oven,” perfectly illustrates this.
Now, the complete Grokking Deep Learning book is available, offering a unique opportunity to learn deep learning from the ground up. With its hands-on approach and insights from a leading practitioner, this book is a valuable resource for anyone serious about mastering deep learning.
5. The Hundred-Page Machine Learning Book by Andriy Burkov
Alt text: “The Hundred-Page Machine Learning Book cover, emphasizing its concise nature, making complex machine learning topics accessible in a short read.”
Consider The Hundred-Page Machine Learning Book as the essential next step after grasping the basics. As highlighted in my book review, it’s the ideal resource for those ready to delve deeper into the mechanics of machine learning algorithms after starting with Machine Learning for Humans.
What sets this book apart is its efficiency in covering key machine learning problems and providing effective solutions, along with the reasoning behind them—all within just 100 pages.
While you could read it in a single day, the depth of content encourages a more paced approach. Learning machine learning requires time and thoughtful consideration.
For those seeking further exploration, QR codes throughout the book link to extra-curricular materials curated by Andriy Burkov, enhancing the learning experience beyond the pages. It’s a powerful, compact machine learning book packed with essential knowledge.
Read for free on the book’s website.
6. Deep Learning (The Deep Learning Book) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Alt text: “Deep Learning Book cover, a definitive and comprehensive guide authored by deep learning pioneers, ideal for advanced study in the field.”
Deep Learning, often referred to as The Deep Learning Book, is the most recent addition to my collection, and I opted for the hard copy edition. This is the book that currently challenges me the most, pushing my understanding to new levels.
I am particularly excited about the foundational math sections at the beginning. My learning has been primarily code-first, which explains the order of books in this guide. However, the core of deep learning and machine learning is applied mathematics. While coding frameworks evolve, the underlying mathematical principles remain constant. Linear algebra, for example, is perpetually fundamental.
Authored by three giants of the deep learning realm—Ian Goodfellow (inventor of GANs), Yoshua Bengio (a pioneer of deep learning), and Aaron Courville (whose academic work is highly cited)—this book delves into every essential deep learning concept.
Remember, machine learning is a broad field. Use these books as a solid base to build your knowledge. Crucially, complement your reading with practical application. Applied knowledge is invaluable, and making mistakes through hands-on projects is a powerful way to learn and solidify your skills.
If I’ve overlooked any essential machine learning books, please contribute to our community by leaving your recommendations in the comments below.
Keep learning and exploring!
PS: For an additional resource, check out the 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 exploring.
