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Mastering pandas by Femi Anthony

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Unsupervised learning algorithms

There are two tasks that we are mostly concerned with in unsupervised learning: dimensionality reduction and clustering.

Dimensionality reduction

Dimensionality reduction is used to help visualize higher-dimensional data in a systematic way. This is useful because our human brains can visualize only three spatial dimensions (and possibly, a temporal one), but most datasets involve much higher dimensions.

The typical technique used in dimensionality reduction is Principal Component Analysis (PCA). PCA involves using linear algebra techniques to project higher-dimensional data onto a lower-dimensional space. This inevitably involves the loss of information, but often by projecting along the correct set and number of ...

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