λFirst Principles AIFPAI

Learn / Machine Learning

Machine Learning

The classical foundation: how learning is defined, trained, evaluated, and regularized.

Overview

Introduction to machine learning: data, features, models, and what learning means.

Core concepts

Data, features, models, and what “learning” means.

Loss & optimization

Objectives, gradients, and why descent works.

Classic algorithms

Linear regression, kNN, trees, SVMs, and more.

Evaluation

Train/val/test, metrics, and failure modes.

Regularization

Bias-variance, overfitting, and constraints.