Yiga AI

Course 5 of 16

Unsupervised Learning

K-means, Hierarchical, PCA, 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.

Sign in and enrol to unlock every lesson. The first lesson is open as a preview.

Module 1 · 5 lessons · 3 hoursK-meansModule 1 of 4 in Unsupervised Learning.

  1. Overview and set-up

    Overview and set-up — K-means in Unsupervised Learning.

    Lesson

    40 min

  2. Core concepts

    Core concepts — K-means in Unsupervised Learning.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — K-means in Unsupervised Learning.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — K-means in Unsupervised Learning.

    Lesson · Locked

    40 min

  5. Module quiz: K-means

    Check what you learned in K-means before moving on.

    Quiz · Locked

    20 min
Module 2 · 5 lessons · 3 hours LockedHierarchicalModule 2 of 4 in Unsupervised Learning.

  1. Overview and set-up

    Overview and set-up — Hierarchical in Unsupervised Learning.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — Hierarchical in Unsupervised Learning.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Hierarchical in Unsupervised Learning.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Hierarchical in Unsupervised Learning.

    Lesson · Locked

    40 min

  5. Module quiz: Hierarchical

    Check what you learned in Hierarchical before moving on.

    Quiz · Locked

    20 min
Module 3 · 5 lessons · 3 hours LockedPCAModule 3 of 4 in Unsupervised Learning.

  1. Overview and set-up

    Overview and set-up — PCA in Unsupervised Learning.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — PCA in Unsupervised Learning.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — PCA in Unsupervised Learning.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — PCA in Unsupervised Learning.

    Lesson · Locked

    40 min

  5. Module quiz: PCA

    Check what you learned in PCA before moving on.

    Quiz · Locked

    20 min
Module 4 · 6 lessons · 5 hours LockedAnomaly detectionModule 4 of 4 in Unsupervised Learning.

  1. Overview and set-up

    Overview and set-up — Anomaly detection in Unsupervised Learning.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — Anomaly detection in Unsupervised Learning.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Anomaly detection in Unsupervised Learning.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Anomaly detection in Unsupervised Learning.

    Lesson · Locked

    40 min

  5. Module quiz: Anomaly detection

    Check what you learned in Anomaly detection before moving on.

    Quiz · Locked

    20 min

  6. Graded project: Unsupervised Learning

    Apply everything from Unsupervised Learning in one graded, portfolio-ready build.

    Graded project · Locked

    120 min

Applied learning project

Make the learning visible.

Use Graded project: Unsupervised Learning 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
Daniel O., Data & analytics

Your instructor

Daniel O.

Data & analytics

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

Brian Okello, Yiga learner

“The course content made machine learning understandable, and the practice kept me moving.”

Brian Okello

Software developer, Kampala

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Your next best step.

Following the order of Data Scientist with Python.

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Overview and set-up

Unsupervised Learning

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