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Unsupervised Learning

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Unsupervised learning is a type of machine learning where the model is trained on unlabeled data—meaning it’s only given input data without any corresponding output labels. The goal is to discover hidden patterns or structures in the data.

Key Points:

  • No labels: The algorithm doesn’t know the “right” answer.
  • Goal: Group or organize data in meaningful ways.
  • Common Algorithms: K-means clustering, hierarchical clustering, principal component analysis (PCA), autoencoders.
  • Example Use Cases:
    • Customer segmentation (grouping users based on behavior)
    • Anomaly detection (finding unusual patterns)
    • Market basket analysis (products often bought together)

Would you like me to compare supervised vs. unsupervised learning side-by-side?