Linear Dimensionality Reduction

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Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

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