For anyone serious about diving into the world of machine learning and data science, books are indispensable tools. Consider this curated list your personal syllabus, featuring the Best Machine Learning Books that have significantly shaped my own journey and continue to be invaluable resources. If you’re looking to build a solid foundation in this rapidly evolving field, these are the books worth investing your time in.
My approach to learning new subjects often starts with immersing myself in a comprehensive book. I read it cover to cover, allowing the core concepts to sink in. Then, I revisit specific sections that resonate or require deeper understanding. The books listed below are packed with such impactful content that I find myself returning to them repeatedly.
The following books are arranged in a roughly ascending order of technical depth. If you’re completely new to machine learning or data science, I recommend starting from the top and progressing downwards. If you already have a background in Python and mathematics, feel free to jump in further down the list.
This isn’t an exhaustive compilation, and there are undoubtedly other excellent books out there. If you believe a particular book deserves a spot on this list, please share it in the comments below for others to discover.
1. Machine Learning for Humans by Vishal Maini and Samer Sabri
This book originated as a series of insightful articles on Medium, crafted by Vishal Maini and Samer Sabri with the goal of demystifying machine learning. They successfully explain complex concepts in an accessible and engaging manner. For those seeking a comprehensive yet approachable introduction to machine learning, “Machine Learning for Humans” is an excellent starting point. It effectively builds a foundational understanding of crucial machine learning concepts, even if you’re a complete beginner. Even experienced machine learning practitioners can benefit from this book, as it provides inspiration for communicating their work in a clear and understandable way.
2. Python for Data Analysis by Wes McKinney
If you’re embarking on a journey into data science or machine learning, you’ll inevitably encounter Pandas, a powerful Python library for data manipulation and analysis. “Python for Data Analysis” stands out because it’s authored by Wes McKinney, the very creator of Pandas. Learning from the source ensures you’re getting the most accurate and insightful guidance. As a machine learning engineer, a significant portion of my work involves using Pandas to preprocess and prepare data for machine learning models. This book is a masterclass in leveraging Pandas for data analysis, cleaning, transformation, and ultimately, for effective data science and machine learning workflows. A deep understanding of Pandas is an invaluable asset for any aspiring data scientist or machine learning practitioner.
3. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
For those seeking a comprehensive and practical resource to get hands-on with machine learning, “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” is an exceptional choice. This book guides you through two of the most prominent machine learning libraries: Scikit-Learn and TensorFlow (now expanded to include Keras as well). It effectively teaches machine learning concepts through practical, coded examples. Each concept is accompanied by runnable code, allowing you to grasp the practical applications of machine learning. You can learn the capabilities of machine learning and then adapt the provided code examples to tackle your own projects and problems. This book is a fantastic resource for anyone wanting to learn by doing and build real-world machine learning applications.
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. Now a researcher at DeepMind, Trask has a knack for explaining complex topics. Even when only a few chapters of “Grokking Deep Learning” were available, I was captivated. I remember meticulously going through each page, learning how to construct a neural network from the ground up using NumPy, a fundamental Python library for numerical computation. Trask’s descriptive analogies, like comparing deep learning hyperparameters to “dials on your oven,” made complex ideas relatable and easy to grasp. I eagerly anticipated each new chapter. Now, the complete book is available, offering a unique opportunity to learn deep learning from the fundamentals, guided by hands-on examples from a leading expert in the field.
5. The Hundred-Page Machine Learning Book by Andriy Burkov
“The Hundred-Page Machine Learning Book” is aptly named as the “start here and continue here” of machine learning. After grasping the basics with “Machine Learning for Humans,” this book is perfect for those eager to delve deeper into the mechanics of machine learning algorithms. As I mentioned in my book review, what I appreciate most is its concise yet comprehensive approach. It covers various machine learning problems, provides effective solutions, and explains the reasoning behind those solutions, all within a remarkably compact 100 pages. While you could read it in a single day, I recommend taking your time to fully absorb the information. Learning machine learning effectively requires time and dedication. If you crave more than the core content, QR codes throughout the book link to additional curated materials by the author.
Read for free on the book’s website.
6. Deep Learning (The Deep Learning Book) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
“Deep Learning,” often referred to as “The Deep Learning Book,” is the most recent addition to my collection and certainly the most challenging – in the best way possible. I opted for the hard copy because this is a book I intend to study thoroughly. It perfectly embodies the principle I mentioned at the beginning: always read a book that stretches your understanding. I am particularly excited about the foundational math sections at the beginning. My learning journey has been primarily code-first, which is reflected in the order of this book list. However, a deep understanding of machine learning, especially deep learning, is rooted in applied mathematics. While coding frameworks may evolve, the underlying mathematical principles remain constant. Linear algebra, for example, is a cornerstone that will always be relevant. Authored by three giants of the deep learning field – 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 the essential deep learning concepts you need to know. It’s an in-depth exploration for serious learners.
Remember, machine learning is a vast and ever-expanding field. Use these books as a strong base to build your knowledge upon. But knowledge alone isn’t enough – it needs to be applied. The best way to truly learn is by getting your hands dirty, experimenting, and yes, making mistakes.
If I’ve overlooked any crucial machine learning books, please feel free to mention them in the comments below. Your recommendations will be valuable for other learners.
Keep learning and keep growing!
PS: For a different perspective, 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 worth checking out.
