Unsupervised learning is a branch of machine learning where algorithms learn patterns from unlabeled data. Unlike supervised learning, there are no predefined labels or outcomes to guide the training process.

📌 Common Types of Unsupervised Learning Algorithms

  1. Clustering Algorithms

    • K-means
      Kmeans
      A popular method for grouping similar data points into clusters.
    • Hierarchical Clustering
      Hierarchical_Clustering
      Builds a tree of clusters to represent data hierarchies.

  2. Dimensionality Reduction

    • Principal Component Analysis (PCA)
      Principal_Component_Analysis
      Reduces the number of variables while preserving data structure.

  3. Anomaly Detection

    • Identifies rare or unusual patterns that deviate from the norm.

📚 Applications

  • Customer segmentation
  • Image compression
  • Fraud detection
  • Data visualization

🔗 Related Resources

For deeper insights, explore our guide on Machine Learning Fundamentals.

Note: All images are placeholders and illustrative only.