What is unsupervised learning?
Unsupervised learning finds structure without labels.
How It Works:
Models infer patterns-such as clusters or latent representations-directly from unlabeled data, using algorithms like K-means, PCA, or autoencoders.
Key Benefits:
- Discovers hidden groupings or features
- Requires no manual labeling
- Supports anomaly detection
Real-World Use Cases:
- Customer segmentation
- Dimensionality reduction for visualization
FAQs
When to use it?
How evaluate?
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