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This course will help you master Clustering in Unsupervised Learning, covering basics, K-Means, Hierarchical Clustering, and more
11 hours of learning
Clustering
Google Analytics
K-Prototype
What You Will Learn
Start by learning about "clustering," an unsupervised learning technique to find patterns in data based on similarities. Then, explore a case study demonstrating clustering's industry applicability.
Topics Covered
Welcome to 'K-Means Clustering'. Building on your knowledge of unsupervised learning and clustering, this session dives into the K-Means algorithm, a key technique for achieving unsupervised clustering.
Topics Covered
Welcome to 'Executing K-Means in Python'. After learning about clustering and the K-Means algorithm, this session will demonstrate implementing K-Means in Python using the Online Retail case study.
Topics Covered
Learn about Hierarchical Clustering, an unsupervised method where you don't pre-define cluster numbers. Instead, visually assess data point similarities to determine the right number of clusters.
Topics Covered
In this session, we will cover two algorithms, K-Mode and K-Prototype clustering, and briefly discuss the DBSCAN algorithm, a density-based method that divides data into high-density subgroups.
Topics Covered
Earn and Share Your Certificate
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Receive a signed and verifiable e-certificate from upGrad upon successfully completing the course.
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