Clustering: k-meansExplains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Clustering: K-MeansCovers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.
Clustering MethodsCovers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Kernel K-means ClusteringExplores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.
Clustering & Density EstimationCovers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.