Unsupervised learning is a type of machine learning where algorithms learn patterns from unlabeled data. Unlike supervised learning, it doesn’t require predefined labels or outcomes. Here's a quick guide to key concepts and techniques:
Core Concepts
- Clustering 🧬: Grouping similar data points (e.g., K-means, DBSCAN)
- Dimensionality Reduction 📊: Simplifying data structure (e.g., PCA, t-SNE)
- Anomaly Detection ⚠️: Identifying rare or unusual patterns
Popular Algorithms
K-means
K-means Clustering
A basic algorithm for partitioning data into k clusters.Hierarchical Clustering
Hierarchical_Clustering
Builds a tree of clusters for nested groupings.Principal Component Analysis (PCA)
Principal_Component_Analysis
Transforms data into principal components for visualization.
Practical Applications
- Customer segmentation
- Recommendation systems
- Image compression
- Fraud detection
Next Steps
Want to dive deeper into supervised learning? Explore our tutorial: /tech/ai/tutorials/supervised-learning
🔍 Tip: Use scatter plots or heatmaps to visualize clustering results!