Phase 3: Production ML Practices · ~90 minutes · Python

Hyperparameter Tuning

Hyperparameters are the knobs you turn before training starts. Turning them well is the difference between a mediocre model and a great one.

Hiring signal: Understanding of hyperparameter tuning internals

What you will learn

Implement grid search, random search, and Bayesian optimization from scratch and compare their sample efficiency
Explain why random search outperforms grid search when most hyperparameters have low effective dimensionality
Build a Bayesian optimization loop using a surrogate model and acquisition function to guide the search
Design a hyperparameter tuning strategy that avoids overfitting the validation set through proper cross-validation

Introduction

Type: Build Language: Python Prerequisites: Phase 2, Lesson 11 (Ensemble Methods) Time: ~90 minutes

Learning Objectives

Implement grid search, random search, and Bayesian optimization from scratch and compare their sample efficiency
Explain why random search outperforms grid search when most hyperparameters have low effective dimensionality
Build a Bayesian optimization loop using a surrogate model and acquisition function to guide the search
Design a hyperparameter tuning strategy that avoids overfitting the validation set through proper cross-validation

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.