Dimensionality Reduction: PCA & LDA

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Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.

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

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

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

Explores principal components, covariance, correlation, choice, and applications in data analysis.

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

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

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

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.