Machine Learning
Classical machine learning encompasses algorithms and techniques for supervised and unsupervised learning tasks. These methods form the backbone of predictive modeling and pattern recognition in data science.1
Topics in This Section
| Topic | Description |
|---|---|
| Supervised Learning | Regression, classification, SVMs, decision trees |
| Unsupervised Learning | Clustering, dimensionality reduction, anomaly detection |
| Ensemble Methods | Random forests, gradient boosting, bagging, stacking |
| Feature Engineering | Data preprocessing, feature selection, transformations |
Learning Path
Supervised Learning → Unsupervised Learning → Feature Engineering → Ensemble Methods
Related Domains
- Need fundamentals first? See 01 - AI Fundamentals
- Ready for neural networks? See 03 - Deep Learning
- Production deployment? See 07 - MLOps & DevOps
References
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/ ↩︎