Yiga AI

Course 7 of 12

Edge & Mobile ML

TensorFlow Lite, Quantisation, On-device inference, and more.

Yiga AI

Advanced en

Advanced · 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.

Machine Learning Foundations

Deep Learning with PyTorch

Computer Vision

NLP with Transformers

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 hoursTensorFlow LiteModule 1 of 4 in Edge & Mobile ML.

  1. Overview and set-up

    Overview and set-up — TensorFlow Lite in Edge & Mobile ML.

    Lesson

    40 min

  2. Core concepts

    Core concepts — TensorFlow Lite in Edge & Mobile ML.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — TensorFlow Lite in Edge & Mobile ML.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — TensorFlow Lite in Edge & Mobile ML.

    Lesson · Locked

    40 min

  5. Module quiz: TensorFlow Lite

    Check what you learned in TensorFlow Lite before moving on.

    Quiz · Locked

    20 min
Module 2 · 5 lessons · 3 hours LockedQuantisationModule 2 of 4 in Edge & Mobile ML.

  1. Overview and set-up

    Overview and set-up — Quantisation in Edge & Mobile ML.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — Quantisation in Edge & Mobile ML.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Quantisation in Edge & Mobile ML.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Quantisation in Edge & Mobile ML.

    Lesson · Locked

    40 min

  5. Module quiz: Quantisation

    Check what you learned in Quantisation before moving on.

    Quiz · Locked

    20 min
Module 3 · 5 lessons · 3 hours LockedOn-device inferenceModule 3 of 4 in Edge & Mobile ML.

  1. Overview and set-up

    Overview and set-up — On-device inference in Edge & Mobile ML.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — On-device inference in Edge & Mobile ML.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — On-device inference in Edge & Mobile ML.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — On-device inference in Edge & Mobile ML.

    Lesson · Locked

    40 min

  5. Module quiz: On-device inference

    Check what you learned in On-device inference before moving on.

    Quiz · Locked

    20 min
Module 4 · 6 lessons · 5 hours LockedLow-bandwidth designModule 4 of 4 in Edge & Mobile ML.

  1. Overview and set-up

    Overview and set-up — Low-bandwidth design in Edge & Mobile ML.

    Lesson · Locked

    40 min

  2. Core concepts

    Core concepts — Low-bandwidth design in Edge & Mobile ML.

    Lesson · Locked

    40 min

  3. Worked example

    Worked example — Low-bandwidth design in Edge & Mobile ML.

    Lesson · Locked

    40 min

  4. Hands-on practice

    Hands-on practice — Low-bandwidth design in Edge & Mobile ML.

    Lesson · Locked

    40 min

  5. Module quiz: Low-bandwidth design

    Check what you learned in Low-bandwidth design before moving on.

    Quiz · Locked

    20 min

  6. Graded project: Edge & Mobile ML

    Apply everything from Edge & Mobile ML in one graded, portfolio-ready build.

    Graded project · Locked

    120 min

Applied learning project

Make the learning visible.

Use Graded project: Edge & Mobile ML 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
Grace A., AI for professions

Your instructor

Grace A.

AI for professions

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

Samuel Mwangi, Yiga learner

“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 Machine Learning Engineer.

See the full chain

Next lesson

Overview and set-up

Edge & Mobile ML

40 min · Resume

Course 8 in the track

Machine Learning Foundations

Linear/logistic regression, Gradient descent, Regularisation, and more.

Intermediate · Course · 4 weeks at 5 hrs/week

Course 9 in the track

Project: Swahili Speech-to-Text Model

Dataset, Fine-tune Whisper, Evaluate, and more.

Advanced · Course · 4 weeks at 5 hrs/week

Course 10 in the track

Monitoring & Drift

Drift detection, Alerts, Retraining, and more.

Advanced · Course · 3 weeks at 5 hrs/week

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What learners say.

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