Three amazing data science books to read in 2023 (if you didn’t manage in 2022)
2022 was a truly amazing year for the machine learning community worldwide! Many long-awaited titles have been released, including new editions of all-time classics.
In this post I want to share with you three 2022 titles that I believe are especially worth reading (not only) this year.
At the end of each section, you’ll find a series of links related to each book, including a link to an e-book, hard copy, free copy and a code repository (if available).
Ready? Let’s dive in! 🌊
A new edition of a true classic from Kevin P. Murphy published by The MIT Press.
The brand new edition contains Python code (in the accompanying repository) and covers countless topics from basic probability to graph neural networks. And… all of the topics are presented from the probabilistic point of view! The book is over 750 pages long (excluding appendices and references), contains rich mathematical explanations, helpful graphs and plots and inspiring exercises.
I love Murphy’s style of writing and I find it clear and appealing even when he discusses complex topics. This book can be challenging, but it’s also quite self-contained. Wherever more background is needed the author provides us with helpful references. The book comes with an extremely rich bibliography that takes almost 33 pages.
The sequel to this book — “Probabilistic Machine Learning: Advanced Topics” — will contain deeper dives on topics like Bayesian inference, generative models, causality and structure discovery. Personally — I cannot wait to get it! If you feel similarly, check this link for the most recent updates!
“Probabilistic Machine Learning: An Introduction” is a great book if you want to broaden, deepen or organize…