There’s lots of content on machine learning out there and sometimes is hard to decide from which material to choose. The content here is what I usually share with others that ask for recommendations. The materials here are those that worked for me on my journey through AI.

Data Science, Stats and Machine Learning

Books

Name Author(s) Notes
Machine Learning Q and AI Sebastian Raschka Great to review topics on recent ML (neural nets)
Machine Learning Engineering Andriy Burkov Overview how models reach production
Super Study Guide: Transformers and Large Language Models Afshine and Shervine Amidi Awesome content on Transformers, specially for visual learners
The Hundred-Page Machine Learning Book Andriy Burkov Overview on ML topics
Hands-On Machine Learning Aurélien Géron Practical ML
How to Lie with Statistics Darrell Huff Classic on statistical thinking

Courses

Name Author(s) Notes
Stanford CS336 Language Modeling from Scratch Percy Liang, Tatsunori Hashimoto In-depth class about LLMs
Stanford CS229 Machine Learning Andrew Ng Classic, theory + math
Coursera Machine Learning Specialization Andrew Ng Most famous course on ML out there

Tutorials

Name Author(s) Notes Link
Intro to Machine Learning Kaggle Hands-on for beginners kaggle
Intermediate Machine Learning Kaggle Hands-on for beginners kaggle

Programming

Name Author(s) Notes
Clean Code Robert C. Martin A classic on how to write maintainable code
Domain-Driven Design Eric Evans Also a classic on great code

Other stuff

Name Type Author(s) Notes
Empire of AI Book Karen Hao Impact of AI on a global scale
Crucial Conversations Book Joseph Grenny and others Tips on how to handle difficult conversations
Freaknomics Book Steven Levitt and Stephen Dubner If you are a curious person, this is a great book
The Phoenix Project Book Gene Kim, Kevin Behr, George Spafford Classic on tech companies organization
The Creative Act: A Way of Being Book Rick Rubin Discussion on creativity
Dracula Book Bram Stooker My favorite book

Last updated: September 2025