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Unsupervised Learning
Description:
The model learns patterns and structures in data without labeled outputs. It identifies hidden relationships or clusters in the data.
Common Use Cases:
Customer segmentation.
Anomaly detection.
Recommendation systems.
Examples:
Clustering (e.g., K-Means, DBSCAN)
Dimensionality Reduction (e.g., PCA, t-SNE)
Generative Models (e.g., GANs for generating new data)

 Semi-Supervised Learning
Description:
Combines a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy.
Common Use Cases:
Image recognition tasks with limited labeled images.
Natural Language Processing (NLP) with partially labeled text.
Examples:
Self-training algorithms.
Graph-based learning.