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AI • Machine Learning • Tech on Instagram: "Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a new coordinate system where the axes (principal components) capture the most variance (which has the most amount of detail/information). The computation behind PCA involves calculating the covariance matrix of the data, followed by an eigenvalue decomposition. The eigenvalues represent the amount of variance captured by each principal component,
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