AI for students · lesson 3 of 24 · free

Training data, model and prediction — AI for students

Every machine-learning system follows the same order. First comes the training data — the labelled examples. Training turns them into a model — the learned patterns. Then the model makes a prediction about something new, such as naming a leaf it has never seen. A good model is always tested on new examples, not the ones it learned from.

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AI tip

Data → model → prediction — “examples go in, training builds the model, the model predicts”

  1. Training data = the examples (500 labelled leaf photos, old emails marked spam).
  2. The model = the learned patterns that training builds.
  3. A prediction = the model’s answer about something NEW (naming a new leaf, flagging a new email). The order is always data → model → prediction.

Use it for: Explaining how any AI system was made, from a spam filter to a plant-spotting app.

Worked example: ⏩ In machine learning, which comes first: old emails marked spam or flagging a new email? → old emails marked spam

  1. Old emails marked spam are training data; flagging a new email is a prediction.
  2. Training data always comes before the prediction, so “old emails marked spam” comes first.

Check: Try it: draw three boxes — data, model, prediction — and fill them in for a “mango or banana” photo sorter ✓

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✏️ Practice

Work it out in your head or on paper, type the answer and press Check. Stuck? Press “Show me how”.

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🏁 Lesson 3 test

20 questions. Check each answer, then go to the next one. Get 16 or more right to pass.

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🖨️ AI Worksheet: Training Data, Model, Prediction

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