🤖Machine Learning
🔍
Unsupervised Learning
Learn clustering, dimensionality reduction, and pattern discovery in unlabeled data
Prerequisites
🌱
Beginner
BeginnerDiscover patterns without labels
What to Learn
- •K-means clustering algorithm
- •Hierarchical clustering
- •Principal Component Analysis (PCA)
- •t-SNE for visualization
- •Anomaly detection basics
Resources
- 📚Coursera ML by Andrew Ng (unsupervised)
- 📚Scikit-learn clustering guide
- 📚Visual introduction to clustering
🌿
Intermediate
IntermediateAdvanced unsupervised techniques
What to Learn
- •DBSCAN and density-based clustering
- •Gaussian Mixture Models (GMM)
- •UMAP for dimensionality reduction
- •Association rule learning
- •Self-organizing maps
Resources
- 📚Pattern Recognition and ML Ch 9
- 📚UMAP paper and documentation
- 📚Anomaly detection in practice
🌳
Advanced
AdvancedCutting-edge unsupervised methods
What to Learn
- •Variational Autoencoders (VAE)
- •Contrastive learning (SimCLR, MoCo)
- •Self-supervised representation learning
- •Deep clustering methods
- •Manifold learning theory
Resources
- 📚Self-supervised learning survey papers
- 📚VAE tutorial by Kingma
- 📚SimCLR and CLIP papers