Explore the fundamentals of hierarchical clustering in this comprehensive 53-minute video tutorial. Delve into the world of unsupervised learning techniques and discover how to group data points into clusters based on similar characteristics. Learn about different types of hierarchical clustering, distance metrics including Euclidean, Manhattan, and Minkowski, as well as similarity measures like Jaccard Index and Cosine Similarity. Understand how to determine the optimal number of clusters and gain practical insights into applying hierarchical clustering in machine learning applications. Perfect for aspiring data scientists and machine learning enthusiasts looking to enhance their knowledge of clustering algorithms and their real-world applications.
Overview
Syllabus
Introduction.
Agenda.
Introduction to Hierarchical Clustering.
Types of Hierarchical Clustering.
Euclidean Distance.
Manhattan Distance.
Minkowski Distance.
Jaccard Index.
Cosine Similarity.
Optimal Number of Clusters.
Summary.
Taught by
Great Learning
