Data Mining: IntroductionCovers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
Supervised Learning OverviewCovers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Clustering MethodsCovers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Clustering: Theory and PracticeCovers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Clustering: k-meansExplains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Clustering: K-means & LDACovers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
Machine Learning FundamentalsCovers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Clustering & Density EstimationCovers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Unsupervised Behavior ClusteringExplores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.