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Course · AI Lab

Data Training

A famous rule of thumb in machine learning is that most of the real work isn't designing clever algorithms — it's preparing good data and training carefully. This overview covers what "training" actually computes and how to know whether a model genuinely learned something useful, illustrated before you move into the interactive modules below.

Slide 1 of 4

Splitting your data before you touch it

Before training begins, a dataset is split into separate portions with strict jobs. The training set is what the model actually learns from. A validation set is held back to tune decisions during development without contaminating the final judgement. A test set is touched only once, at the very end, to produce an honest, unbiased score — because the moment you start adjusting your model based on test results, that score stops being trustworthy.

Training set (~70%) — the model learns from thisValidationTestwhat the model sees and adjusts ontunes settingsduring trainingfinal honestgrade — seen onceThe test set is never touched until the very end — that is what keeps the score honest.
Training data teaches the model; validation tunes it along the way; the test set gives one honest final score.

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Interactive modules for Data Training

Signal Start

Working With Data

The unglamorous but critical part of ML: what makes data usable for training, how to clean it, and how to split it so your evaluation is honest.

6 guided lessons →

Pattern Builder

How to Train a Model

The "train data" course: what training actually computes, hands-on JavaScript labs for loss, gradient descent, and the learning rate — with real running code and a real console.

7 guided lessons →