Generative AI Engineering
University partner: Partner University (sample).
Machine learning and language model theory from university faculty; building, evaluating and deploying AI features from engineers at an AI product company.
Who this course is for
Developers and data professionals with Python, and strong engineering graduates.
What you will be able to do
Graduates are ready for AI engineer, machine learning engineer and applied scientist roles at the junior level.
Careers this course serves
Jobs this course leads to
Roles from partner employers that name this course's certification.
Syllabus, by who teaches it
Progress is tracked separately for theory and practical, and both are assessed.
- A1Machine learning foundations
Supervised learning, evaluation and the limits of models.
- A2How language models work
Tokens, embeddings, attention and training.
- A3Retrieval and grounding
Vector search, chunking and citation.
- A4Responsible AI
Privacy, bias, safety and evaluation.
- B1Working with model APIs
Prompting, structured outputs, tools and cost control.
- B2Retrieval-augmented applications
A document assistant with evaluation harness.
- B3Fine-tuning and small models
When and how to adapt a model.
- B4Deployment
Serving, monitoring and guarding an AI feature in production.
- B5Industry project
An AI feature built to a partner company's brief and reviewed by its engineers.
Ready for Generative AI Engineering?
Ask about the next batch, fees and whether your employer can sponsor the seat.