Course 8 of 12

Machine Learning Foundations

Linear/logistic regression, Gradient descent, Regularisation, 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.

Machine Learning Foundations

Deep Learning with PyTorch

Computer Vision

NLP with Transformers

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 hoursLinear/logistic regressionModule 1 of 4 in Machine Learning Foundations.

Overview and set-up
Overview and set-up — Linear/logistic regression in Machine Learning Foundations.
Lesson
40 min
Core concepts
Core concepts — Linear/logistic regression in Machine Learning Foundations.
Lesson · Locked
40 min
Worked example
Worked example — Linear/logistic regression in Machine Learning Foundations.
Lesson · Locked
40 min
Hands-on practice
Hands-on practice — Linear/logistic regression in Machine Learning Foundations.
Lesson · Locked
40 min
Common pitfalls
Common pitfalls — Linear/logistic regression in Machine Learning Foundations.
Lesson · Locked
40 min
Module quiz: Linear/logistic regression
Check what you learned in Linear/logistic regression before moving on.
Quiz · Locked
20 min

Module 2 · 6 lessons · 3.7 hours LockedGradient descentModule 2 of 4 in Machine Learning Foundations.

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

Module 3 · 6 lessons · 3.7 hours LockedRegularisationModule 3 of 4 in Machine Learning Foundations.

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

Module 4 · 7 lessons · 5.7 hours LockedMetricsModule 4 of 4 in Machine Learning Foundations.

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

Applied learning project

Make the learning visible.

Use Graded project: Machine Learning Foundations 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 Machine Learning Engineer.

See the full chain

Next lesson

Overview and set-up

Machine Learning Foundations

40 min · Resume

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

Course 11 in the track

Deep Learning with PyTorch

Tensors, Autograd, CNNs, and more.

Intermediate · Course · 4 weeks at 5 hrs/week

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