<ul><li>Principle Component Analysis (PCA) is a powerful technique that helps achieve dimensionality reduction.</li><li>Reducing dimensions offers benefits, such as faster computation, less storage and memory usage, and noise reduction.</li><li>PCA involves projecting data into a lower-dimensional space while preserving as much information as possible.</li><li>To find the optimal projection vector, PCA uses the eigenvalue equation, eigenvectors, and the covariance matrix.</li></ul>

Beautiful Math Behind Principle Component Analysis.

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