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.