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Data Science & ML track

Hyperparameter Optimization

Grid/random/Bayesian search, tuning workflows and leaking validation.

6 concepts4 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

Parameters vs Hyperparameters

Concept · Hands-on lab included

2

Grid Search

Concept · Hands-on lab included

3

Random Search

Concept · Hands-on lab included

4

Bayesian Optimization

Concept · Hands-on lab included

5

Early Stopping and Pruning

Concept · Hands-on lab included

6

Search Spaces and Tuning Pipelines

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

Grid Search, Sized to Survive

25 min · intermediate · Grid Search

Random vs Grid at the Same Budget

25 min · intermediate · Random Search

Optuna: Fewer Trials, Same Winner

30 min · advanced · Bayesian Optimization

Early Stop and Prune a Training Loop by Hand

30 min · intermediate · Early Stopping and Pruning

Continue

Next modules

Keep your learning streak going with related modules.

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Mathematics

Linear algebra, calculus and notation needed for machine learning.

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Statistics

Distributions, estimators, hypothesis testing and statistical inference.

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Python

Scientific Python stack: environments, typing, numpy-style thinking.

DevOpsForge - Data Science & ML · Hyperparameter Optimization
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