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Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.
Explores the use of Gaussian Mixture Models for transitioning from clustering to classification, covering binary classification, parameter estimation, and optimal Bayes classifier.
Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.
