Orchard Vision

PythonIntermediateabout 6 hours16 lessons

Recommended first: Pebble Beach

Build the brain of a smart fruit-sorting machine. Sensors measure every apple, pear and plum that rolls past: weight, size, colour, sugar. Lesson by lesson you turn those numbers into a working learner: you clean and scale the data, teach it to recognise fruit by its nearest neighbours, test it honestly, fit a price line with gradient descent, and predict ripeness with logistic regression. A live lab shows decision regions, loss curves and a confusion matrix redrawing themselves from your code.

Start the first lesson

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Learning path

Each lesson adds one working part to your build.

Chapter 1

Meet the data

Turn raw fruit measurements into clean numbers a machine can learn from.

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Chapter 2

Learning by example

Sort a mystery fruit by asking which known fruit it resembles, then test that honestly.

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Chapter 3

Learning with numbers

Fit a price line, let gradient descent find it, then predict ripeness with probabilities.

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Chapter 4

Trust your model

Choose thresholds on purpose, spot over- and underfitting, invent better features and ship the pipeline.

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