computer-science

Data Science & Machine Learning

Watch algorithms learn from data in real time — from linear regression fitting lines to K-means discovering clusters to gradient descent navigating loss landscapes.

data sciencemachine learningregressionclusteringPCAgradient descentdecision tree

Data science is the art of extracting knowledge from data, and machine learning is its most powerful tool. Behind every recommendation engine, self-driving car, and language model lies a surprisingly small set of core algorithms — linear regression, clustering, decision trees, gradient descent, and dimensionality reduction.

These simulations strip away the abstraction and let you watch algorithms think. See a regression line snap to data points in real time. Watch K-means clusters form and shift. Observe a decision tree carve up feature space. Follow gradient descent as it navigates a loss landscape. Witness PCA compress high-dimensional data into its essential components.

5 interactive simulations

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Decision Tree Classifier

Watch a decision tree carve feature space into regions — see how splits are chosen to maximize information gain and classify data

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Gradient Descent Optimization

Watch gradient descent navigate a loss landscape — see how learning rate and momentum affect convergence to the minimum of a function

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K-Means Clustering Algorithm

Watch K-means discover clusters in real time — see centroids shift and data points reassign as the algorithm converges to a partition

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Linear Regression & Least Squares Fit

Watch a regression line snap to data points in real time — explore how least squares minimization finds the best-fit line through noisy data

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PCA Dimensionality Reduction

Watch PCA compress high-dimensional data into its essential components — see eigenvectors emerge and variance explained accumulate