Unsupervised learning is a machine learning approach where algorithms learn patterns from unlabeled data. Here’s a guide to advanced concepts and techniques:

Key Topics Covered

  • Clustering Algorithms 📊

    • K-Means
    • Hierarchical Clustering
    • DBSCAN
    • Spectral Clustering
    • Gaussian Mixture Models

  • Dimensionality Reduction 🔍

    • Principal Component Analysis (PCA)
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
    • Autoencoders

  • Anomaly Detection ⚠️

    • Isolation Forest
    • Autoencoders for outlier detection
    • One-Class SVM

  • Association Rule Learning 📚

    • Apriori algorithm
    • FP-Growth

Practical Applications

  1. Customer Segmentation 🧑‍🤝‍🧑
    • Group users based on behavior patterns
  2. Image Compression 📷
    • Reduce file sizes using PCA
  3. Recommendation Systems 🎯
    • Collaborative filtering with clustering
  4. Data Preprocessing 🧼
    • Feature scaling and normalization

Example Code Snippets

from sklearn.cluster import KMeans  
kmeans = KMeans(n_clusters=3)  
kmeans.fit(data)  
labels = kmeans.predict(data)

Expand Your Knowledge

For a deeper dive into foundational concepts, check out our Machine Learning Overview Tutorial.

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