Investigate unlabeled structure with clustering and dimensionality reduction while making geometry, scaling, validation, and interpretation limits explicit.
Tutorials
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K-means does not discover the real categories hiding in your data. It draws geometric boundaries around points that happen to sit close together. Those…
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Most beginners ask the wrong question first. They ask, "Which clustering algorithm is better?" The real question is, "Which question am I actually trying…
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Most people assume PCA "removes unimportant features." It does not. It rotates your data into new directions ranked by variance, then drops the quietest…
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Clustering always returns groups. Even on random noise, even on data with no structure worth naming, every algorithm will happily partition your points and…
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You learned K-means, and it felt clean: pick a number of groups, let centroids pull points inward, and read off the labels. Then you hit a dataset with…
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Anomaly detection is not "find the weird rows." It is a decision about what counts as normal, made before you ever run an algorithm. Get that decision…
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Every clustering algorithm will happily partition pure noise into tidy groups. Run k-means on random data, and it returns clean circles of assigned points.…
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Picture a point sitting exactly between two well-separated blobs of data. K-means will force it into one group or the other, even when the data could…
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You run PCA on your data and get one picture. You run t-SNE on the same data and get a completely different one. One shows two messy blobs. The other shows…
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