What AI & Machine Learning Actually Are

You hear the words every day now. AI this, machine learning that, deep learning, LLMs, neural networks. Maybe you've nodded along in a meeting while quietly unsure whether those are four names for the same thing or four different things. Maybe a tool at work suddenly has "AI" stamped on it and you can't tell if that's real or marketing. You're not behind — almost nobody was handed a clear, hype-free explanation of what these words actually mean.

This guide fixes that, from zero. No math, no jargon you haven't been given first, and no breathless claims about robots. By the end you'll have a working mental model of what AI and machine learning really are, the one big idea that separates them from ordinary code, and an honest picture of what today's AI can and can't do — so you can read past the hype and reason about this stuff on your own.

How to read this

Total beginner? Read in order — each phase builds on the last, and none assumes the one before was "obvious."
Just want the buzzwords untangled? Phase 1 alone clears up the AI / ML / deep learning / LLM soup in one sitting.

The phases

AI vs ML vs Deep Learning vs LLMs — the nested-circles mental model and a plain-language definition of each buzzword, so the soup finally separates.
Rules vs Learning — the one idea everything rests on: old code is rules a human wrote; machine learning learns the rules from examples. When that wins, and when it doesn't.
What AI Is and Isn't — the honest framing: today's AI is powerful pattern-matching and prediction, not understanding — which is exactly why it's sometimes confidently wrong.

This is the front door to the AI & ML track. The deeper mechanics — how a model is actually trained, how to call a model from your own code, and the day-to-day craft of working with data — each get their own guide and build on the model you install here.