Course 15 of 16

Feature Engineering

Encoding, Scaling, Selection, 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 hoursEncodingModule 1 of 4 in Feature Engineering.

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

Module 2 · 5 lessons · 3 hours LockedScalingModule 2 of 4 in Feature Engineering.

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

Module 3 · 5 lessons · 3 hours LockedSelectionModule 3 of 4 in Feature Engineering.

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

Module 4 · 6 lessons · 5 hours LockedPipelinesModule 4 of 4 in Feature Engineering.

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

Applied learning project

Make the learning visible.

Use Graded project: Feature Engineering 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

Grace A.

AI for professions

Shows working professionals how to use AI responsibly inside everyday workflows.

“The examples felt relevant to public service and worked on the connection I actually have.”
Samuel Mwangi
Public service officer, Nairobi

Continue with

Your next best step.

Following the order of Data Scientist with Python.

See the full chain

Next lesson

Overview and set-up

Feature Engineering

40 min · Resume

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 courseTime Series Analysis Next courseMachine Learning Explainability

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.