Course 12 of 16

Supervised Learning with scikit-learn

Regression, Classification, Train/test, and more.

Yiga AI

Intermediate en

Intermediate · Course · 4 weeks at 5 hrs/week

Duration: 4 weeks
Effort: 5 hrs/week
Lessons: 20
Hands-on exercises: 20
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 · 25 lessons · 17 hours · 1 graded project

1000 minutes of guided lesson time.

Module 1 · 6 lessons · 3.7 hoursRegressionModule 1 of 4 in Supervised Learning with scikit-learn.

Overview and set-up
Overview and set-up — Regression in Supervised Learning with scikit-learn.
Lesson
40 min
Core concepts
Core concepts — Regression in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Worked example
Worked example — Regression in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Regression in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Regression in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Module quiz: Regression
Check what you learned in Regression before moving on.
Quiz · Locked
20 min

Module 2 · 6 lessons · 3.7 hours LockedClassificationModule 2 of 4 in Supervised Learning with scikit-learn.

Overview and set-up
Overview and set-up — Classification in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Core concepts
Core concepts — Classification in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Worked example
Worked example — Classification in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Classification in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Classification in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Module quiz: Classification
Check what you learned in Classification before moving on.
Quiz · Locked
20 min

Module 3 · 6 lessons · 3.7 hours LockedTrain/testModule 3 of 4 in Supervised Learning with scikit-learn.

Overview and set-up
Overview and set-up — Train/test in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Core concepts
Core concepts — Train/test in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Worked example
Worked example — Train/test in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Train/test in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Train/test in Supervised Learning with scikit-learn.
Lesson · Locked
40 min
Module quiz: Train/test
Check what you learned in Train/test before moving on.
Quiz · Locked
20 min

Module 4 · 7 lessons · 5.7 hours LockedMetricsModule 4 of 4 in Supervised Learning with scikit-learn.

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

Applied learning project

Make the learning visible.

Use Graded project: Supervised Learning with scikit-learn 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

Supervised Learning with scikit-learn

40 min · Resume

Course 13 in the track

Model Evaluation & Tuning

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

Intermediate · Course · 3 weeks at 5 hrs/week

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

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What learners say.

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