Unsupervised learning is a branch of machine learning where algorithms learn patterns from unlabeled data. Unlike supervised learning, it does not rely on predefined labels or outcomes. This guide explores key concepts and techniques in unsupervised learning.

Common Algorithms 📊

  • K-means Clustering
    A simple iterative algorithm that partitions data into k clusters.

    KMeans Clustering

  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
    Identifies clusters based on density and proximity.

    DBSCAN Algorithm

  • Hierarchical Clustering
    Builds a tree of nested clusters to represent data relationships.

    Hierarchical Clustering

  • Principal Component Analysis (PCA)
    Reduces dimensionality while preserving data variance.

    PCA Dimensionality

  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
    Visualizes high-dimensional data in 2D/3D space.

    t-SNE Visualization

Applications 🚀

  • Customer segmentation
  • Anomaly detection
  • Data compression
  • Pattern recognition

Tips for Success 💡

  1. Normalize data before applying clustering algorithms
  2. Use visualization tools to interpret results
  3. Validate cluster quality with metrics like Silhouette Score

For deeper insights into unsupervised learning techniques, visit our Advanced Topics guide.