Learning Resource Library Starter

This page is the map of Computer Learning Academy itself: what each collection on the site is for, when to reach for it, and how to combine them into a study routine that actually sticks. It is for anyone who has landed on a few of our pages and wants to see how the pieces fit. If you are completely new, the start here page is the two-minute orientation; this page is the full floor plan.

Tutorials: learn the concepts

The tutorials collection is where ideas get explained from zero. Start with what is artificial intelligence and machine learning in plain english for foundations, generative AI explained for beginners for how modern tools work, and the prompting sequence — prompt engineering basics, then the clear prompt framework — for the skill you will use daily. Read tutorials when something is unfamiliar; they are built to be read once, slowly.

Cheat sheets: keep the answers handy

Cheat sheets are the companion format: dense reference pages you keep open while working rather than read end to end. The AI vocabulary cheat sheet decodes the jargon, the prompt patterns cheat sheet gives you reusable prompt structures, and the machine learning terms cheat sheet supports the more technical tutorials. A good pairing is one tutorial to learn, one cheat sheet to retain.

Learning paths: follow a sequence

Learning paths solve the "what order?" problem by chaining tutorials and practice into a route with a destination. The AI beginner roadmap is the default starting path; AI at work serves professionals applying AI in their current job; AI engineering for developers serves people who already code. Pick one path and let it decide your next step — that single decision removes most of the indecision that stalls self-taught learners.

Project starters: build something real

The project starters collection gives you preconfigured foundations so a project begins with building, not setup. The Python starter matches the data and AI material; the React and Vite starter suits web projects. Reach for these the moment a path or tutorial says "now practice."

Models hub: know what you are using

The models hub tracks the major AI models and what each is good for, with references like the embedding models cheat sheet for deeper technical choices. Visit it when choosing a tool or when a model name in the news means nothing to you.

Best practices: use AI responsibly

Best practices covers judgment rather than mechanics: how to verify AI answers before relying on them, and building productive AI workflows that survive past the novelty phase. These pages matter most once AI is in your real work, where mistakes have costs.

News: stay current without drowning

The news section summarizes what actually changed in AI and why it matters to learners. Once a week is plenty — the fundamentals in the tutorials change far slower than the headlines.

A weekly routine that combines them

A sustainable pattern using every collection: one tutorial early in the week, its matching cheat sheet kept open during practice, two or three sessions working on a project from a starter, your learning path consulted to pick what is next, and a short pass through news at week's end. Beyond this site, our best free AI learning resources lists the external courses and communities worth adding, and the resources collection begins with the five tools to have on day one.