Unsupervised Learning
Unsupervised Learning is a machine learning approach in which a model learns patterns, structures, or relationships from data without relying on predefined labels or target outputs.
What is Unsupervised Learning?
In unsupervised learning, the model analyzes unlabeled data to discover meaningful patterns on its own. Common techniques include clustering, which groups similar data points, and dimensionality reduction, which simplifies complex datasets while preserving important information.
Why is Unsupervised Learning Important?
Unsupervised learning allows organizations to analyze large amounts of data when labeled examples are unavailable or expensive to create. It can reveal hidden structures, identify unusual patterns, and generate useful insights that may not be apparent through manual analysis.
Common use cases
Unsupervised Learning is commonly used in customer segmentation, anomaly detection, recommendation systems, exploratory data analysis, clustering, dimensionality reduction, and pattern discovery.