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Course
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Unsupervised Learning and Clustering with R
Unsupervised learning reveals patterns in data without predefined labels. This course will teach you how to use R to apply clustering, dimensionality reduction, and anomaly detection techniques to explore and analyze unlabeled datasets.
What you'll learn
Making sense of unlabeled data is a common challenge in real-world analytics, where predefined categories or outcomes aren’t available. In this course, Unsupervised Learning and Clustering with R, you’ll gain the ability to uncover hidden structures in unlabeled datasets using core unsupervised learning techniques. First, you’ll explore the difference between supervised and unsupervised learning, and identify where unsupervised methods are best applied. Next, you’ll discover how to implement clustering algorithms like k-means, hierarchical clustering, and DBSCAN, and how to evaluate their performance. Finally, you’ll learn how to reduce high-dimensional data using techniques like PCA, t-SNE, and UMAP, and apply anomaly detection methods such as isolation forests and one-class SVMs. When you’re finished with this course, you’ll have the skills and knowledge of unsupervised learning needed to analyze complex, unlabeled datasets and extract meaningful insights in R.
Table of contents
About the author
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
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