Machine Learning Intro

Overview

Intro to supervised learning: from problem framing to evaluation. Build and validate simple models end‑to‑end.

Understand data splitting, metrics, overfitting and the model lifecycle.

Syllabus

• Framing ML problems and dataset preparation
• Train/validation/test splits and leakage risks
• Linear/logistic regression and decision trees basics
• Feature engineering and normalization patterns
• Metrics: accuracy, precision/recall, ROC/AUC
• Overfitting/underfitting and regularization basics
• Baseline first: compare against simple heuristics
• Hands‑on: train/evaluate a simple classifier