Support Vector Machines: Basics and Applications

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Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

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

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.