Unsupervised learning is a type of machine learning where algorithms learn patterns from unlabeled data. Unlike supervised learning, it doesn't rely on predefined labels or outcomes. Here's a quick overview:

Key Concepts 🔍

  • Objective: Discover hidden structures in data without explicit guidance
  • Common Techniques:
    • Clustering (e.g., K-means, DBSCAN)
    • Dimensionality reduction (e.g., PCA, t-SNE)
    • Anomaly detection
    • Association rule learning

Applications and Examples 🌍

  • Customer segmentation using clustering algorithms
    customer_segmentation
  • Image compression through dimensionality reduction
    image_compression
  • Recommendation systems based on association patterns
    recommendation_systems

Further Learning 📚

Want to dive deeper? Explore our Machine Learning Essentials course for foundational concepts.