Core paradigms: supervised, unsupervised, and reinforcement learning. Typical workflow: problem framing → data → features → model → eval → deployment.
Common algorithms
- Tree ensembles (XGBoost, Random Forest)
- Linear/Logistic regression, SVM
- Neural networks (CNN/RNN/Transformers)