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Machine learning, made visual

CodeWithPurpose lessons that explain core ML ideas through live demos and clear visuals. Click any topic to explore — no setup required.

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Student exploring machine learning with CodeWithPurpose

1. What Machine Learning Is

3 chapters

What the field actually does, what data has to look like, and what it means for a model to learn.

FoundationsInteractive

What Is Machine Learning?

Some problems cannot be solved by writing rules, no matter how many you write. Stack rules one at a time and watch each one buy you less than the last.

10 min read

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FoundationsInteractive

Features and Labels

Data does not arrive ready to learn from. See how two measurements that are each useless on their own combine into one that works.

11 min read

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FoundationsInteractive

How a Model Learns

Drag one number, watch total error rise and fall, and find the bottom of the bowl yourself.

12 min read

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2. Getting It Right

4 chapters

The two questions you can ask, why you hold data back, and the failure that catches everyone.

FoundationsInteractive

Classification vs Regression

Categories or numbers? The line between them is a choice you make — and chopping a number into categories can make your accuracy look better while the model gets less useful.

11 min read

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FoundationsInteractive

Train/Test Split

A model graded on the data it studied will always flatter itself. Re-roll the split and watch the score you would have reported swing by six points.

12 min read

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FoundationsInteractive

Overfitting and Underfitting

The central failure of machine learning. Add bends to a curve until it passes through every training point — and gets dramatically worse at everything else.

12 min read

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ClassificationInteractive

Measuring a Model

Accuracy is the first number everyone reaches for and the first one that will mislead you. Drag a decision threshold and watch precision and recall trade against each other.

13 min read

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3. Models You Can Picture

3 chapters

Three real algorithms, each simple enough to run by hand on paper — and each wrong in its own instructive way.

AlgorithmsInteractive

k-Nearest Neighbours

A model that learns nothing at all, and still beats a careless one. Move a student across the chart and watch five neighbours vote on their future.

12 min read

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AlgorithmsInteractive

Decision Trees

Twenty questions, asked in the right order. Pick the split yourself, see how much confusion each one clears, and find out why the greedy choice is not always the best one.

13 min read

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AlgorithmsInteractive

Random Forests

One deep tree memorises its training data. Two hundred of them, each deliberately handicapped, do not. Add trees one at a time and watch the boundary settle down.

12 min read

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4. Making the Number Honest

4 chapters

How to evaluate a model beyond a single score.

EvaluationInteractive

Cross-Validation

Chapter five left you with a score that was partly luck. Rotate which slice is held back, average the five results, and watch a lottery ticket turn into a measurement.

12 min read

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EvaluationInteractive

Data Leakage

The bug that makes your model look brilliant in testing and useless in production. Four realistic pipelines, one of them honest — find the leak before you scroll to the answer.

13 min read

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EvaluationInteractive

Class Imbalance

When one answer is rare, accuracy stops meaning anything and most fixes quietly make the model worse. Rebalance a fraud dataset and watch what each repair actually costs.

12 min read

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EvaluationInteractive

Baselines

Before you can say a model is good you need something for it to be better than. Race four models against a rule a child could write, and find out which ones lose.

11 min read

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5. Learning Without Labels

3 chapters

Every model so far needed somebody to write the right answer down first. Three that do not, and the new ways each of them can be wrong.

UnsupervisedInteractive

Clustering

Every model up to here needed somebody to write the right answer down first. Step a k-means run one move at a time and watch the groups it settles on change with where you started it.

12 min read

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UnsupervisedInteractive

Dimensionality Reduction

Forty columns is not forty times more information than one, and the extra ones cost you accuracy. Rotate the line a cloud of points is flattened onto and watch how much of the spread survives.

12 min read

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UnsupervisedInteractive

Anomaly Detection

The cases you care about most are the ones you have almost no examples of, which is exactly why training a classifier on them fails. Move a cut-off and watch caught fraud trade against false alarms.

11 min read

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6. How the Fitting Actually Works

5 chapters

Under the fit is a search, and under the search is a step size. What the optimiser is doing, what it costs, and what happens to a model after it ships.

PreprocessingInteractive

Feature Scaling

Distance arithmetic does not know that one column is in years and another in pounds, so the big column quietly decides every answer. Rescale the axes and watch a nearest-neighbour verdict flip.

11 min read

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OptimisationInteractive

Gradient Descent

Trying every slope works with one parameter and never works again after that. Set a step size and watch the same search converge, crawl, or throw itself off the hill entirely.

13 min read

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EvaluationInteractive

Regularisation

Overfitting has a fix that is not "collect more data", and it works by charging the model rent on its own complexity. Turn the penalty up and watch coefficients fall over one at a time.

12 min read

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ModelsInteractive

Neural Networks

A neural network is not a brain. It is the straight-line fit you already understand, stacked, with one small bend between the layers — add hidden units one at a time and watch a flat boundary learn to curve.

14 min read

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PracticeInteractive

From Notebook to Production

A model that scored well on Tuesday's data is not a system, and the gap between the two is where most projects quietly die. Age a deployed model month by month and watch the score decay before anyone files a bug.

12 min read

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Open source

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Every lesson here was written by a student. Propose your own topic, or fix one that confused you. Drafts stay invisible until they are ready, so nothing you start can break the site.

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