For anyone venturing into the world of machine learning, books are indispensable tools. They provide a structured, in-depth understanding that you often can’t get from scattered online resources. I’ve always believed in tackling challenging reads to truly grow, and the books listed below have been instrumental in shaping my machine learning and data science journey.
These aren’t just books I’ve read once and shelved. They are foundational texts I continually revisit, each time gleaning new insights. If you’re serious about learning machine learning or data science, investing your time in these books will be invaluable.
When approaching a new subject, my strategy is simple: find a highly recommended book and read it cover to cover. Then, I delve deeper into the concepts that resonate most. These books are packed with such resonant concepts, making them exceptional resources for anyone in the field.
The books are ordered to help you progress smoothly. If you’re completely new to machine learning or data science, start from the top. If you already have a solid foundation in Python and mathematics, you might want to begin further down the list.
This list isn’t exhaustive, and there are certainly other excellent books out there. If you have a recommendation that you believe should be included, please leave a comment below to share it with others!
Machine Learning for Humans by Vishal Maini and Samer Sabri
“Machine Learning for Humans” began as a popular series on Medium, aiming to demystify machine learning concepts in an accessible way. Vishal Maini and Samer Sabri have succeeded brilliantly. This book serves as an excellent entry point into the world of machine learning.
If you’re seeking a comprehensive resource to build a foundational understanding of crucial machine learning concepts without prior experience, this book is tailored for you. Even seasoned machine learning practitioners can benefit from reading it. It offers fresh perspectives on explaining complex topics in an engaging and understandable manner, which is invaluable for anyone looking to communicate their work effectively.
Python for Data Analysis by Wes McKinney
Embarking on a journey into data science or machine learning inevitably leads you to Pandas, the powerful Python library for data analysis. “Python for Data Analysis” is the definitive guide, authored by Wes McKinney, the creator of Pandas himself. Learning from the source ensures you’re getting the most accurate and insightful knowledge.
As a machine learning engineer, a significant portion of my time is dedicated to using Pandas. It’s the go-to tool for manipulating and preparing data to feed into machine learning models. This book is your manual to mastering Pandas for data analysis, cleaning, transformation, and ultimately, leveraging it effectively in data science and machine learning projects.
Whether you’re a data scientist or a machine learning engineer, a deep understanding of Pandas is indispensable. This book will elevate your data manipulation skills to the next level.
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
If you are diving into machine learning and need a comprehensive, practical resource, “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” is your one-stop guide. Aurélien Géron expertly walks you through two of the most powerful machine learning ecosystems: Scikit-Learn and TensorFlow (now updated to include Keras). The book emphasizes learning by doing, with machine learning concepts explained through practical, coded examples.
Each concept is accompanied by code, allowing you to immediately apply what you learn. You’ll gain a solid grasp of machine learning capabilities and be able to adapt the provided code examples to tackle your own unique problems. This hands-on approach makes complex theories tangible and accelerates your learning process.
Pre-order latest edition on Amazon.
Grokking Deep Learning by Andrew Trask
My deep learning journey began with Udacity’s Deep Learning Nanodegree, where Andrew Trask was one of the instructors. Now a researcher at DeepMind, Andrew has a gift for explaining complex topics. “Grokking Deep Learning” captures this gift perfectly.
Even in its early release with just a few chapters, I was captivated. Sitting on my couch, I meticulously went through each page, learning to build a neural network from scratch using NumPy, Python’s numerical powerhouse. Andrew’s engaging analogies made abstract machine learning concepts relatable and understandable.
His analogy, “Deep learning hyperparameters can be tuned like the dials on your oven,” perfectly illustrates his knack for simplification. I eagerly awaited each new chapter release. Now, the complete book is available, offering a comprehensive guide to deep learning.
This book provides a unique opportunity to learn deep learning from the ground up, guided by hands-on examples from a leading practitioner in the field.
The 100-Page Machine Learning Book by Andriy Burkov
“The 100-Page Machine Learning Book” is, in my words, the essential “start here and continue here” guide to machine learning. After grasping the basics with “Machine Learning for Humans,” if you’re eager to delve deeper into the mechanics of machine learning algorithms, this book is your next logical step.
What stands out is its conciseness and practicality. It effectively covers common problems in machine learning, providing clear solutions along with the reasoning behind them—all within just 100 pages. In my book review, I highlighted its ability to deliver a substantial amount of knowledge in a compact format.
While you could read it in a single day, I recommend taking your time to absorb the dense information. Learning machine learning, like any complex subject, requires time and patience. If the 100 pages leave you wanting more, QR codes throughout the book link to additional curated content by the author, enriching your learning experience.
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,” often referred to as “The Deep Learning Book,” is the latest addition to my collection, and I opted for the hard copy – a testament to its significance. This book perfectly embodies the idea of tackling challenging material for growth.
I’m particularly excited to delve into the foundational math sections. My learning approach has been primarily code-first, which is reflected in the order of this book list. However, the core of deep learning and machine learning is applied mathematics. While coding frameworks evolve, the underlying math remains constant. Linear algebra, for instance, will always be linear algebra.
Authored by three giants in the deep learning field—Ian Goodfellow (inventor of GANs), Yoshua Bengio (a pioneer of deep learning), and Aaron Courville (whose academic work is extensively cited)—this book provides an exhaustive exploration of essential deep learning concepts. It truly dives deep into the subject matter.
Remember, machine learning is a vast and evolving field. Use these books as a strong base to build your knowledge upon, and crucially, complement your reading with practical application. Applied knowledge is invaluable. The best learning often comes from making mistakes and actively engaging with projects.
If I’ve overlooked any pivotal books, please share your recommendations in the comments below—your insights will benefit fellow learners.
Keep exploring and learning!
P.S. For those who prefer video content, there’s a YouTube version of this article 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.
