🎉 Unsupervised Machine Learning Algorithms

Unsupervised learning is a branch of machine learning where the model learns patterns from unlabeled data. Here are key algorithms and their applications:

🧠 Key Concepts

• Clustering: Grouping similar data points (e.g., K-Means, DBSCAN)
• Dimensionality Reduction: Simplifying data structure (e.g., PCA, t-SNE)
• Anomaly Detection: Identifying outliers (e.g., Isolation Forest)
• Association Rule Learning: Discovering relationships (e.g., Apriori)

🔄 Popular Algorithms

1. K-Means Clustering

• Purpose: Partition data into K clusters
• Visual:
• Use Case: Customer segmentation, image compression
• Link: Tutorial: K-Means in Action

2. Principal Component Analysis (PCA)

• Purpose: Reduce dimensions while preserving variance
• Visual:
• Use Case: Data visualization, feature extraction
• Link: How PCA Works

3. DBSCAN (Density-Based Spatial Clustering)

• Purpose: Find dense regions in data
• Visual:
• Use Case: Outlier detection, spatial data analysis

🚀 Practical Applications

• Marketing: Group customers by behavior
• Biology: Analyze gene expression data
• Image Recognition: Compress images via clustering
• Recommendation Systems: Discover user patterns

✅ Best Practices

• Normalize data before clustering
• Use elbow method for optimal K in K-Means
• Validate results with silhouette score

Explore more about unsupervised learning fundamentals or dive into semi-supervised techniques.