What’s a Good Roadmap for Getting Started in AI and Machine Learning?

Andrej Karpathy’s beginner-friendly material is worth checking out, and Sutskever’s recommended reading list can give you ideas for what to study next. Use those as a map rather than trying to consume everything at once. Pick one structured course or book, work through the exercises, and keep a small project going alongside it.

An AI-generated study plan can be useful for organizing resources, but it won’t replace advice from people who have actually learned the material or done the work. Use it to fill in gaps and suggest practice exercises, then verify the explanations and focus on hands-on learning.

A good learning path is: Python fundamentals, data handling, basic math, classical machine learning, neural networks, and finally larger topics like language models or computer vision. Don’t wait until you know everything before building things. Try projects such as predicting house prices, classifying images, analyzing text, or automating a repetitive task. Projects will also help you decide whether you actually enjoy the work.