For Students
A practical guide for students starting out in AI and machine learning — split into four parts so you can jump straight to what you need. New here? Start with Set up your environment, then follow the learning path.
Set up your environment
Install the tools once — package manager, Git, Python (uv), a local model runner, an AI coding assistant, and cloud GPUs. Copy-paste commands for macOS, Linux, and Windows.
Open →The learning path
What to learn, in order — from Python and the essential math to classical ML, deep learning, generative models, and LLMs & agents. Plus courses to anchor on and the concepts to know early.
Open →Habits & experiments
The engineering habits that keep you productive, and the tooling that keeps experiments reproducible and organized once you're running more than a couple.
Open →Reading, writing & talks
The skills that matter as much as the code: reading papers actively, writing clearly as you go, and giving a research talk people remember.
Open →Questions? If you're a student getting started and something here is unclear — or you just want a pointer on where to go next — feel free to reach out.