Python for AI Engineering
A short course covering Python fundamentals oriented toward AI application work: environment setup, API clients, structured data handling, and basic testing. Proposed catalog entry; enrollment opens only after curriculum, delivery owner, pricing, and schedule are approved.
- Python
- HTTP clients
- JSON/schemas
- Basic automated tests
- Git
This is fee-based Project-Based Training — not a paid internship. Registering interest is not enrollment and does not collect payment. Fee details will be shared before enrollment.
Program facts
- Duration
- 1 month
- Level
- Foundational
- Weekly commitment
- Approximately 8–12 hours per week (subject to final schedule)
- Delivery
- Online
- Availability
- Coming soon — interest registration available
- Fee
- Fee details will be shared before enrollment
- Mentor / supervisor
- Instructor or delivery owner is assigned before a course is marked open for enrollment.
- Delivery owner
- Assigned before enrollment opens
- Certificate policy
- Certificate policy is confirmed when the course opens for enrollment.
Audience, prerequisites, and selection
Audience
- Learners preparing for AI application projects
- Developers needing a Python refresh for AI APIs
Prerequisites
- Some prior programming experience recommended
- No prior AI experience required
Curriculum
Curriculum status: forthcoming / proposed. Topics below are indicative and not a finalized enrollment curriculum until marked approved.
- Python environment and toolingfoundational
- Data structures for API workfoundational
- HTTP clients and error handlingfoundational
- Working with JSON and schemasfoundational
- Basic automated testsfoundational
Learning outcomes
- Write clear Python modules for API-driven workflows
- Handle errors and structured responses reliably
- Add basic tests around critical functions
Project specification
Build a small Python tool that calls an AI API with validated inputs and tested error paths.
- Problem statement · Illustrative
- A developer needs a small Python tool that calls an AI API safely, validates inputs, and fails clearly when the API or payload is invalid.
- Primary user workflow · Illustrative
- Provide input → validate → call AI API → parse structured response → print or store result → handle errors.
- Functional scope · Illustrative
- Environment setup, HTTP client usage, JSON handling, basic tests, and clear error messages.
- Technical components · Illustrative
- Python modules, configuration via environment variables, API client, validation helpers, and tests.
- Data requirements · Illustrative
- Sample prompts and responses only. Paid model credits are not assumed unless published for a cohort.
- Testing expectations · Illustrative
- Valid request path, invalid input rejection, and at least one simulated API failure.
- Known limitations · Illustrative
- Not a full AI product. Retrieval, agents, and production observability are out of scope.
- What counts as completion · Illustrative
- Working script or CLI, basic tests, short documentation, and a walkthrough of error handling.
Examples
- Build a small CLI or script that calls an AI API with validated inputs and tested error paths
Deliverables
- Working CLI or script
- Basic automated tests
- Short technical notes
Example or assigned project work—not a published student achievement or employment guarantee. Student work is not assumed for commercial use without appropriate agreements. Sections marked illustrative are representative examples, not a fixed commercial brief.
Evaluation criteria
How work is reviewed — clarity of decisions matters as much as a working MVP.
- Exercises and a short capstone script
- Passing automated checks where provided
Evaluation focuses on observable engineering evidence. Exact rubrics are published per program when approved.
01
Main workflow correctness
Does the primary user journey work as specified?
02
Code comprehension
Can the participant explain what the code does and why?
03
Architecture explanation
Are component boundaries and trade-offs articulated clearly?
04
Input validation
Are invalid inputs rejected or handled safely?
05
Testing
Are expected behavior and at least one failure mode checked?
06
Edge-case handling
Does the work account for realistic non-happy-path conditions?
07
Documentation
Can another engineer set up, run, and understand the MVP?
08
Demonstration
Is the working result shown with an honest account of limitations?
Frequently asked questions
Ready to build something real?
Choose a pathway, review a program, and register interest when enrollment opens.