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Principal Component Analysis

Serrano.Academy via YouTube

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Overview

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This conceptual course explains Principal Component Analysis as a dimensionality reduction technique. It uses visual examples to introduce variance, covariance matrices, linear transformations, eigenvalues, and eigenvectors, with minimal formulas.

Syllabus

Introduction
Taking a picture
Dimensionality Reduction
Housing Data
Mean
Variance?
Covariance matrix
Linear Transformations
Eigenstuff
Eigenvalues
Eigenvectors
Principal Component Analysis PCA
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Taught by

Serrano.Academy

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