Unsupervised Learning
No labels, no teacher. The algorithm finds structure on its own. Implement K-Means, DBSCAN, and Gaussian Mixture Models from scratch and compare their clustering behavior. Evaluate cluster quality using the silhouette score and the elbow method to select the optimal K. Explain when DBSCAN outperforms K-Means and identify which algorithm handles non-spherical clusters and outliers. Build an anomaly detection pipeline using clustering methods to flag points that deviate from normal patterns. Every ML lesson so far has assumed labeled data: "here is an input, here is the correct output." In the real world, labels are expensive. A hospital has millions of patient records but no one has manually tagged each one with a disease category. An e-commerce site has millions of user sessions but no one has hand-labeled customer segments. A security team has network logs but nobody has flagged every anomaly. Unsupervised learning finds patterns without being told what to look for. It groups similar data points, discovers hidden structures, and surfaces anomalies. If supervised learning is learning from a textbook with an answer key, unsupervised learning is staring at raw data until the patterns reveal themselves. The catch: without labels, you cannot directly measure "right" or "wrong." You need different tools to evaluate whether the structure your algorithm found is meaningful. Clustering assigns each data point to a…
Unsupervised Learning: No labels, no teacher. The algorithm finds structure on its own. Implement K-Means, DBSCAN, and Gaussian Mixture Models from scratch…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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