Course 1 of 12
MLOps FundamentalsExperiment tracking (MLflow), Pipelines, Versioning, and more.
Intermediate · Course · 4 weeks at 5 hrs/week
17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project
Career track
Machine Learning Engineer
12 courses, 100 hours, ending in Project: Swahili Speech-to-Text Model.
Yiga AI
Intermediate → Advanced en
Intermediate → Advanced · Career Track · 4–5 months at 10 hrs/week
- Duration
- 4–5 months
- Effort
- 10 hrs/week
- Courses
- 12
- Hands-on exercises
- 237
- Projects
- 12
- Certificate
- Included
Your pace≈ 4–5 months · 198 hours total
What you'll learn
12 courses in this track
- 01MLOps FundamentalsIntermediate · 16.7 hours · 20 lessonsExperiment tracking (MLflow) · Pipelines · Versioning · +1 more
- 02NLP with TransformersIntermediate · 16.7 hours · 20 lessonsAttention · BERT · Fine-tuning · +1 more
- 03Project: Fraud Detection System End-to-EndAdvanced · 19.3 hours · 24 lessonsData · Model · Deployment · +1 more
- 04Reinforcement Learning IntroAdvanced · 14 hours · 16 lessonsAgents & environments · Q-learning · Policy gradients · +1 more
- 05Computer VisionIntermediate · 16.8 hours · 21 lessonsImage classification · Object detection · Transfer learning · +1 more
- 06Data Pipelines for MLIntermediate · 14 hours · 16 lessonsFeature stores · Airflow · Data validation · +1 more
- 07Edge & Mobile MLAdvanced · 14 hours · 16 lessonsTensorFlow Lite · Quantisation · On-device inference · +1 more
- 08Machine Learning FoundationsIntermediate · 16.7 hours · 20 lessonsLinear/logistic regression · Gradient descent · Regularisation · +1 more
- 09Project: Swahili Speech-to-Text ModelAdvanced · 19.3 hours · 24 lessonsDataset · Fine-tune Whisper · Evaluate · +1 more
- 10Monitoring & DriftAdvanced · 14 hours · 16 lessonsDrift detection · Alerts · Retraining · +1 more
- 11Deep Learning with PyTorchIntermediate · 19.3 hours · 24 lessonsTensors · Autograd · CNNs · +1 more
- 12Model Deployment & ServingIntermediate · 16.7 hours · 20 lessonsDocker · FastAPI · Batch vs real-time · +1 more
About this track
What you’ll learn.
1
Machine Learning Foundations
2
Deep Learning with PyTorch
3
Computer Vision
4
NLP with Transformers
Skills you’ll gain
AI
How this compares
Comparable to DataCamp Machine Learning Engineer · Coursera ML Specialization. This track is 198 hours of study — about 4–5 months at 10 hrs/week.
Career outcomes
Turn learning into forward motion.
Build job-ready evidence for roles that use these skills every day.
Curriculum
A clear path, course by course.
Complete the courses in order so each new skill builds on the last.
- 1View course
- 2View course
Course 2 of 12
NLP with TransformersAttention, BERT, Fine-tuning, and more.
Intermediate · Course · 4 weeks at 5 hrs/week
17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project
- 3View course
Course 3 of 12
Project: Fraud Detection System End-to-EndData, Model, Deployment, and more.
Advanced · Course · 4 weeks at 5 hrs/week
19 hours · 24 lessons · 24 exercises · 4 quizzes · 1 project
- 4View course
Course 4 of 12
Reinforcement Learning IntroAgents & environments, Q-learning, Policy gradients, and more.
Advanced · Course · 3 weeks at 5 hrs/week
14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project
- 5View course
Course 5 of 12
Computer VisionImage classification, Object detection, Transfer learning, and more.
Intermediate · Course · 4 weeks at 5 hrs/week
17 hours · 21 lessons · 21 exercises · 4 quizzes · 1 project
- 6View course
Course 6 of 12
Data Pipelines for MLFeature stores, Airflow, Data validation, and more.
Intermediate · Course · 3 weeks at 5 hrs/week
14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project
- 7View course
Course 7 of 12
Edge & Mobile MLTensorFlow Lite, Quantisation, On-device inference, and more.
Advanced · Course · 3 weeks at 5 hrs/week
14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project
- 8View course
Course 8 of 12
Machine Learning FoundationsLinear/logistic regression, Gradient descent, Regularisation, and more.
Intermediate · Course · 4 weeks at 5 hrs/week
17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project
- 9View course
Course 9 of 12
Project: Swahili Speech-to-Text ModelDataset, Fine-tune Whisper, Evaluate, and more.
Advanced · Course · 4 weeks at 5 hrs/week
19 hours · 24 lessons · 24 exercises · 4 quizzes · 1 project
- 10View course
Course 10 of 12
Monitoring & DriftDrift detection, Alerts, Retraining, and more.
Advanced · Course · 3 weeks at 5 hrs/week
14 hours · 16 lessons · 16 exercises · 4 quizzes · 1 project
- 11View course
Course 11 of 12
Deep Learning with PyTorchTensors, Autograd, CNNs, and more.
Intermediate · Course · 4 weeks at 5 hrs/week
19 hours · 24 lessons · 24 exercises · 4 quizzes · 1 project
- 12View course
Course 12 of 12
Model Deployment & ServingDocker, FastAPI, Batch vs real-time, and more.
Intermediate · Course · 4 weeks at 5 hrs/week
17 hours · 20 lessons · 20 exercises · 4 quizzes · 1 project
Applied learning project
Finish with work you can show.
Bring the track together in Model Deployment & Serving. Apply what you have learned to a practical challenge and explain the decisions behind your solution.
- Machine Learning Foundations
- Deep Learning with PyTorch
- Computer Vision
Learn from experts
Mariam K.
Software & cloud
Helps new developers build reliable products through clear explanations and guided practice.
“I use what I learned in my work now, not in some distant future project.”
Frequently asked questions
Before you begin.
Do I need prior experience?
This track is designed for intermediate → advanced learners. Start with the first course and follow the numbered path so each skill builds naturally.
How long will the track take?
The track contains 12 courses and about 198 hours of learning. Most learners finish in around 4–5 months at 10 hrs/week.
Can I learn on my phone?
Yes. Yiga lessons are designed to work across phones and computers, so you can keep learning on the device available to you.
Will I earn a credential?
You can earn a shareable Yiga credential after completing the required learning and assessment activities in the track.
Is financial support available?
Scholarship and financial-aid options are available for eligible learners. Contact the Yiga support team before enrolling for guidance.
Ready when you are
Start this track today.
Begin with course 1 of 12, MLOps Fundamentals, and work through the path at your own pace.
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