Category
Unsupervised learning
Related lectures (209)
Unsupervised Behavior Clustering
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.
Unsupervised Learning: Clustering Methods
Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.
Clustering Methods
Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Clustering: Unsupervised Learning
Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.
Supervised Learning Overview
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Clustering: Hierarchical and K-means Methods
Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.
Clustering: Theory and Practice
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Unsupervised Machine Learning: Clustering Basics
Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.
Dimensionality Reduction: PCA and LDA
Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.
Clustering Methods: K-means and DBSCAN
Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.