PCA and Kernel PCA

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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 artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.

Explores Principal Component Analysis for dimensionality reduction and unsupervised feature selection.

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Covers PCA, Kernel PCA, and autoencoders for dimensionality reduction in data analysis.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.