Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.
Unsupervised learning algorithms
Some of the most common algorithms used in unsupervised learning include:
- Anomaly detection
- Neural Networks
- Approaches for learning latent variable models such as
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