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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.
Covers the evaluation of clustering methods, including K-means clustering and the use of evaluation metrics to determine the optimal number of clusters.
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Explores the stochastic blockmodel, spectral clustering, and non-parametric understanding of blockmodels, emphasizing metrics for comparing graph models.
