Traditional Machine Learning

Traditional machine learning refers to classical statistical and algorithmic approaches for building predictive models from data, typically without deep neural networks. It includes supervised methods like linear regression and decision trees, unsupervised techniques like clustering, and ensemble methods. These approaches often rely on feature engineering and simpler model architectures compared to deep learning.

Also known as: Classical Machine Learning, Statistical Learning, Non-Deep Learning ML, Traditional ML, Shallow Learning

Why learn Traditional Machine Learning?

Developers should learn traditional ML for interpretable, efficient solutions in structured data problems like credit scoring, customer segmentation, or fraud detection. It's essential when computational resources are limited, data is small, or model explainability is critical for regulatory compliance. These methods provide a strong foundation before advancing to deep learning.

Learning Resources

• docs: Scikit-learn Documentation
• course: Coursera: Machine Learning by Andrew Ng
• book: Introduction to Statistical Learning
• tutorial: Kaggle: Traditional ML Tutorials
• video: YouTube: StatQuest with Josh Starmer