Unsupervised Learning

➺ Core Techniques:

  • Pattern recognition
  • Dimensionality reduction
  • Density estimation
  • Anomaly detection

    • ➺ Implementation Details:

    Clustering Methods

    • K-Means clustering
    • Hierarchical clustering
    • DBSCAN
    • Gaussian Mixture Models

    Dimensionality Reduction

    • Principal Component Analysis (PCA)
    • t-SNE
    • UMAP
    • Autoencoders

    Association Rules

    • Apriori algorithm
    • FP-Growth
    • ECLAT

    ➺ Applications:

    Business Use Cases

    • Market segmentation
    • Customer behavior analysis
    • Fraud detection
    • Network analysis

    Technical Applications

    • Feature learning
    • Image compression
    • Recommendation engines
    • Anomaly detection