Hierarchical Clustering | Cosine Similarity | Types of Hierarchical Clustering | Great Learning

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Clustering is a very important part of machine learning that governs many applications that we have today. It is a concept that is widely used especially when we require the use of unsupervised learning techniques. Hierarchical clustering talks about how we could go on to pick up multiple data points and combine or separate them into individual clusters containing similar characteristics. Since it is very important for all you machine learning enthusiasts to understand this in detail, we here at Great Learning have come up with this course to help you get started with hierarchical clustering and to understand it completely.

00:00 Introduction
02:23 Agenda
04:50 Introduction to Hierarchical Clustering
15:55 Types of Hierarchical Clustering
31:48 Euclidean Distance
34:28 Manhattan Distance
37:20 Minkowski Distance
39:20 Jaccard Index
43:22 Cosine Similarity
47:40 Optimal Number of Clusters
50:30 Summary

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