# How a Model Learns (Training, in Plain English)

You keep hearing that a model was "trained on data," and somewhere in your head that word *training* does a
lot of quiet, magical work. A machine reads some examples and then... knows things? That gap - between "fed
it data" and "now it can answer" - is where the mystery lives, and where most explanations either hand-wave
or drown you in calculus.

This guide closes that gap with zero math. By the end you'll have a working mental picture of what
training really does: it's the slow, patient tuning of a giant pile of numbers until the model's
guesses line up with answers we already know. Not magic. Tuning.

## How to read this

- **Want the one-sentence version that finally clicks?** Read [Phase 1](01-data-weights-predictions.md)
 - the whole idea fits in three boxes.
- **Want it to actually make sense?** Read in order. Each phase builds on the last: what a model *is*,
  how it *learns*, and why we *test* it the way we do.

## The phases

1. **[Data → Weights → Predictions](01-data-weights-predictions.md)** - the core mental model: a model
   is a big bundle of adjustable numbers, and predicting is running an input through them.
2. **[Learning by Being Wrong](02-learning-by-being-wrong.md)** - the training loop in plain words:
   guess, measure how wrong, nudge the numbers, repeat - millions of times.
3. **[Overfitting & Why Test Sets Exist](03-overfitting-and-test-sets.md)** - memorizing vs. truly
   learning, the train/validation/test split, and why a model is only as fair as its data.

> This guide deliberately stops at the *intuition*. The actual recipes - picking algorithms, tuning
> the dials, the real math of gradients - belong in a follow-up. Here we're after the mental model that
> makes all of that finally make sense.