Support Vector Clustering: SVC

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Page 3 of 4

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

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

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

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

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

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

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.