# Regression Practice

> ML preparation set.

- Stable ID: `regression-practice`
- Area: machine-learning
- Kind: practice
- Timebox: 35 minutes

Connect squared loss, gradients, normal equations, and ridge regularization.

## Instructions

1. Track matrix dimensions through every expression.

## Ordered items

1. [Linear regression knowledge check](https://mlprep.iwase.dev/machine-learning/regression/google-mlcc-linear-regression-quiz/) — `google-mlcc-linear-regression-quiz` (10 min)
2. [Linear regression objective and gradient](https://mlprep.iwase.dev/machine-learning/regression/original-ml-regression/) — `original-ml-regression` (8 min)
3. [Ridge normal equations and effect](https://mlprep.iwase.dev/machine-learning/regression/original-ml-ridge/) — `original-ml-ridge` (8 min)

## Completion

Derive the unregularized and ridge objectives and updates.