What is Unsupervised Learning?
Unsupervised Learning — Training an AI on data that has no labels, allowing it to find hidden patterns and structures on its own.
Unsupervised learning finds hidden patterns in data without labeled examples. Common applications include customer segmentation, anomaly detection, and dimensionality reduction. It is valuable when you have lots of data but no predefined categories to train against.
Frequently Asked Questions
What business problems does unsupervised learning solve?
Customer segmentation, fraud/anomaly detection, recommendation systems, data compression, and discovering hidden patterns in large datasets that humans might miss.
How is it different from supervised learning?
Supervised learning needs labeled data (correct answers). Unsupervised learning works with unlabeled data and discovers structure on its own. No human needs to categorize the data first.
What are common unsupervised learning algorithms?
K-means clustering, hierarchical clustering, principal component analysis (PCA), autoencoders, and DBSCAN are the most widely used algorithms.