Course 16 of 16

Machine Learning Explainability

SHAP, Feature importance, Fairness, 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 hoursSHAPModule 1 of 4 in Machine Learning Explainability.

Overview and set-up
Overview and set-up — SHAP in Machine Learning Explainability.
Lesson
40 min
Core concepts
Core concepts — SHAP in Machine Learning Explainability.
Lesson · Locked
40 min
Worked example
Worked example — SHAP in Machine Learning Explainability.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — SHAP in Machine Learning Explainability.
Lesson · Locked
40 min
Module quiz: SHAP
Check what you learned in SHAP before moving on.
Quiz · Locked
20 min

Module 2 · 5 lessons · 3 hours LockedFeature importanceModule 2 of 4 in Machine Learning Explainability.

Overview and set-up
Overview and set-up — Feature importance in Machine Learning Explainability.
Lesson · Locked
40 min
Core concepts
Core concepts — Feature importance in Machine Learning Explainability.
Lesson · Locked
40 min
Worked example
Worked example — Feature importance in Machine Learning Explainability.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Feature importance in Machine Learning Explainability.
Lesson · Locked
40 min
Module quiz: Feature importance
Check what you learned in Feature importance before moving on.
Quiz · Locked
20 min

Module 3 · 5 lessons · 3 hours LockedFairnessModule 3 of 4 in Machine Learning Explainability.

Overview and set-up
Overview and set-up — Fairness in Machine Learning Explainability.
Lesson · Locked
40 min
Core concepts
Core concepts — Fairness in Machine Learning Explainability.
Lesson · Locked
40 min
Worked example
Worked example — Fairness in Machine Learning Explainability.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Fairness in Machine Learning Explainability.
Lesson · Locked
40 min
Module quiz: Fairness
Check what you learned in Fairness before moving on.
Quiz · Locked
20 min

Module 4 · 6 lessons · 5 hours LockedCommunicating resultsModule 4 of 4 in Machine Learning Explainability.

Overview and set-up
Overview and set-up — Communicating results in Machine Learning Explainability.
Lesson · Locked
40 min
Core concepts
Core concepts — Communicating results in Machine Learning Explainability.
Lesson · Locked
40 min
Worked example
Worked example — Communicating results in Machine Learning Explainability.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Communicating results in Machine Learning Explainability.
Lesson · Locked
40 min
Module quiz: Communicating results
Check what you learned in Communicating results before moving on.
Quiz · Locked
20 min
Graded project: Machine Learning Explainability
Apply everything from Machine Learning Explainability in one graded, portfolio-ready build.
Graded project · Locked
120 min

Applied learning project

Make the learning visible.

Use Graded project: Machine Learning Explainability 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

Joel N.

Career readiness

Connects technical learning to portfolios, interviews and confident career moves.

“The track removed the uncertainty. I knew what to learn next and what to show employers.”
Amina Hassan
Career switcher, Dar es Salaam

Continue with

Your next best step.

Following the order of Data Scientist with Python.

See the full chain

Next lesson

Overview and set-up

Machine Learning Explainability

40 min · Resume

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