Machine Learning Foundations

Machine Learning

Data, objectives, validation, regularization, and classical models.

Modified

October 16, 2026

Planned: supervised and unsupervised learning, loss functions, bias–variance tradeoff, cross-validation, leakage, metrics, feature engineering, and interpretability.

---
title: "Machine Learning Foundations"
description: "Data, objectives, validation, regularization, and classical models."
categories: [Machine Learning]
---

Planned: supervised and unsupervised learning, loss functions, bias–variance tradeoff, cross-validation, leakage, metrics, feature engineering, and interpretability.