Definition
Unsupervised learning identifies hidden structures, clusters, or distributions within raw data autonomously. Common methods include clustering, dimensionality reduction, and generative modeling. It is essential for exploratory data analysis and feature extraction when labeled datasets are scarce or expensive. By finding intrinsic relationships, these models help organize information and prepare data for downstream supervised tasks.
Summary
A machine learning technique where models learn patterns from unlabeled data without explicit guidance or correct answers.
Key Concepts
- Clustering
- Dimensionality Reduction
- Pattern Recognition
- Labeled Data Absence
Use Cases
- Customer segmentation
- Anomaly detection
- Topic modeling in NLP