Course 13 of 16

Model Evaluation & Tuning

Cross-validation, Grid search, ROC/AUC, and more.

Yiga AI

Intermediate en

Intermediate · Course · 3 weeks at 5 hrs/week

Duration: 3 weeks
Effort: 5 hrs/week
Lessons: 16
Hands-on exercises: 16
Projects: 1
Language: en

What you’ll learn

Skills you can put to work.

Python for Data Science Refresher

Statistical Thinking

Supervised Learning with scikit-learn

Unsupervised Learning

Syllabus

Every module, step by step.

4 modules · 21 lessons · 14 hours · 1 graded project

840 minutes of guided lesson time.

Module 1 · 5 lessons · 3 hoursCross-validationModule 1 of 4 in Model Evaluation & Tuning.

Overview and set-up
Overview and set-up — Cross-validation in Model Evaluation & Tuning.
Lesson
40 min
Core concepts
Core concepts — Cross-validation in Model Evaluation & Tuning.
Lesson · Locked
40 min
Worked example
Worked example — Cross-validation in Model Evaluation & Tuning.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Cross-validation in Model Evaluation & Tuning.
Lesson · Locked
40 min
Module quiz: Cross-validation
Check what you learned in Cross-validation before moving on.
Quiz · Locked
20 min

Module 2 · 5 lessons · 3 hours LockedGrid searchModule 2 of 4 in Model Evaluation & Tuning.

Overview and set-up
Overview and set-up — Grid search in Model Evaluation & Tuning.
Lesson · Locked
40 min
Core concepts
Core concepts — Grid search in Model Evaluation & Tuning.
Lesson · Locked
40 min
Worked example
Worked example — Grid search in Model Evaluation & Tuning.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Grid search in Model Evaluation & Tuning.
Lesson · Locked
40 min
Module quiz: Grid search
Check what you learned in Grid search before moving on.
Quiz · Locked
20 min

Module 3 · 5 lessons · 3 hours LockedROC/AUCModule 3 of 4 in Model Evaluation & Tuning.

Overview and set-up
Overview and set-up — ROC/AUC in Model Evaluation & Tuning.
Lesson · Locked
40 min
Core concepts
Core concepts — ROC/AUC in Model Evaluation & Tuning.
Lesson · Locked
40 min
Worked example
Worked example — ROC/AUC in Model Evaluation & Tuning.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — ROC/AUC in Model Evaluation & Tuning.
Lesson · Locked
40 min
Module quiz: ROC/AUC
Check what you learned in ROC/AUC before moving on.
Quiz · Locked
20 min

Module 4 · 6 lessons · 5 hours LockedImbalanced dataModule 4 of 4 in Model Evaluation & Tuning.

Overview and set-up
Overview and set-up — Imbalanced data in Model Evaluation & Tuning.
Lesson · Locked
40 min
Core concepts
Core concepts — Imbalanced data in Model Evaluation & Tuning.
Lesson · Locked
40 min
Worked example
Worked example — Imbalanced data in Model Evaluation & Tuning.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Imbalanced data in Model Evaluation & Tuning.
Lesson · Locked
40 min
Module quiz: Imbalanced data
Check what you learned in Imbalanced data before moving on.
Quiz · Locked
20 min
Graded project: Model Evaluation & Tuning
Apply everything from Model Evaluation & Tuning in one graded, portfolio-ready build.
Graded project · Locked
120 min

Applied learning project

Make the learning visible.

Use Graded project: Model Evaluation & Tuning to apply this course to a practical challenge. You’ll finish with a clear solution and the reasoning behind your choices.

Apply the course’s core skillMake and explain practical decisionsCreate evidence you can share

Your instructor

Daniel O.

Data & analytics

Turns real African data problems into practical lessons learners can apply immediately.

“The course content made machine learning understandable, and the practice kept me moving.”
Brian Okello
Software developer, Kampala

Continue with

Your next best step.

Following the order of Data Scientist with Python.

See the full chain

Next lesson

Overview and set-up

Model Evaluation & Tuning

40 min · Resume

Course 14 in the track

Time Series Analysis

Trends & seasonality, ARIMA, Prophet, and more.

Intermediate · Course · 4 weeks at 5 hrs/week

Course 15 in the track

Feature Engineering

Encoding, Scaling, Selection, and more.

Intermediate · Course · 3 weeks at 5 hrs/week

Course 16 in the track

Machine Learning Explainability

SHAP, Feature importance, Fairness, and more.

Intermediate · Course · 3 weeks at 5 hrs/week

Learner reviews

What learners say.

Write a review

Loading reviews…

Previous courseSupervised Learning with scikit-learn Next courseTime Series Analysis

Keep going

Other courses in this track.

View full track

Introduction to Deep Learning cover illustration

Data

20 lessons · 16.7h · Intermediate

Introduction to Deep Learning

Neural networks, Keras, Training, and more.

Project: Crop Disease Detection from Images cover illustration

Data

24 lessons · 19.3h · Advanced

Project: Crop Disease Detection from Images

Data collection, CNN, Evaluation, and more.

Data

16 lessons · 14h · Intermediate

Statistical Thinking

Distributions, Sampling, Bootstrapping, and more.

Data

17 lessons · 14.1h · Intermediate

Data Scientist Interview Prep

Case studies, Coding rounds, Stats questions, and more.