AI Programming with Python Course
Learn backend Python for AI engineering. Build APIs, use FastAPI, async Python, Pydantic, databases, testing and LLM integrations before moving to RAG, agents, LangGraph, MCP and production AI systems.
- Live, hands-on instruction
- 40 HoursLive, hands-on instruction
- Backend Python for AI
- 8 ModulesBackend Python for AI
- Document Intelligence API
- 1 CapstoneDocument Intelligence API
- To Production AI Engineering
- BridgeTo Production AI Engineering
What is AI Programming with Python?
AI Programming with Python is a practical bridge course that teaches the backend Python skills needed to build AI applications. Learners work with APIs, async Python, Pydantic, FastAPI, databases, testing and document-processing workflows before moving into RAG, agents and production AI engineering.
Why Normal Python Isn't Enough for AI Engineering
Loops, functions and notebooks are a starting point, not an endpoint. Production AI applications are backend services — they need reliable API clients, structured data models, async calls that don't block, validation, error handling, database persistence, tests and logs, all wired together as real backend services.
API clients
Structured data models
Async calls
Validation
Error handling
Database persistence
Tests and logs
Backend services
Who Should Attend
Prerequisite: basic Python syntax, functions and collections. Complete beginners should take a Python basics course first.
Software Developers
Already write code and want the backend Python patterns AI applications need.
Backend Developers
Comfortable with services and APIs — ready to add AI-specific patterns like LLM calls and validation.
QA Automation Engineers
Know scripting and testing — this adds API integration, async Python and backend structure.
Data Analysts Moving to AI Engineering
Know Python for analysis and want to build backend, API-driven AI services instead.
Students with Basic Python Knowledge
Know Python syntax and want a structured path into real application development.
Professionals Who Know Basics but Cannot Build APIs
Comfortable with Python scripts, not yet with services, APIs or async code.
Developers Preparing for Production AI Engineering
Want to arrive at that programme already comfortable with FastAPI, async Python and APIs.
Skills You Will Learn
40-Hour Curriculum
Eight modules, sequenced from Python foundations to a working AI engineering mini-project.
Python Foundations for AI Apps
- →Collections
- →Functions
- →Modules
- →File handling
- →JSON
- →CSV
- →Exceptions
- →Pathlib
- →Environment variables
Professional Python
- →OOP
- →Type hints
- →Dataclasses
- →Pydantic
- →Project structure
- →Reusable services
APIs and Integrations
- →REST
- →HTTP methods
- →Headers
- →Bearer tokens
- →JSON
- →httpx
- →Timeouts
- →Retries
- →API errors
Async Python
- →async/await
- →asyncio.gather
- →Async HTTP clients
- →Streaming basics
- →Blocking vs non-blocking code
FastAPI for AI Backends
- →Endpoints
- →Request/response models
- →Validation
- →Uploads
- →Dependencies
- →Error handling
- →Health checks
Databases and Persistence
- →SQLite
- →PostgreSQL basics
- →SQL
- →Storing summaries, metadata and conversations
Testing and Logging
- →pytest
- →Fixtures
- →Mocking API calls
- →Testing async functions
- →Logging
- →Debugging
AI Engineering Mini-Project
- →Document ingestion
- →LLM API call
- →FastAPI endpoint
- →Database storage
Hands-On Labs
Build an API client with httpx
Validate LLM output with Pydantic
Build a FastAPI endpoint
Add timeout and error handling
Store an LLM result in SQLite/PostgreSQL
Write pytest tests for an AI API workflow
Final Project: Document Intelligence API
The capstone project touches every module — file handling, Pydantic validation, an LLM API call, database persistence, a FastAPI endpoint, and tests and logging.
How This Prepares You for Production AI Engineering
This bridge course prepares learners for RAG, LangGraph, tool calling, AI agents, MCP and deployment modules by strengthening the Python foundation first. Once you can confidently build a FastAPI endpoint, call an LLM, validate the response and store it in a database, Production AI Engineering builds directly on those same skills.
Tools and Libraries
Readiness Checklist
By the end of this course, you will be able to:
What This Course Does Not Cover
This is a backend/application Python bridge course, not a data science or machine learning course.
Frequently Asked Questions
Is this a beginner Python course?+
No. This is a bridge course for learners who already know basic Python syntax, functions and collections. Complete beginners should take a Python basics course first — this programme does not teach Python fundamentals from zero.
Is this course useful before Production AI Engineering?+
Yes. It is designed specifically to prepare learners for Production AI Engineering by building the backend Python skills — APIs, async Python, Pydantic, FastAPI, databases, testing — that programme assumes participants already have.
Do I need data science or machine learning knowledge?+
No. This course does not cover data science, pandas-heavy analysis, machine learning model training, or deep learning. It focuses entirely on backend and application Python for building AI-powered services.
Will I learn FastAPI and APIs?+
Yes. FastAPI backend development is a full module, and REST APIs, HTTP methods, and API integrations with httpx are covered in a dedicated module earlier in the course.
Will I build an AI project?+
Yes. The final project is a Document Intelligence API — you upload a document, extract and chunk its text, call an LLM, validate the response with Pydantic, store the result in a database, and expose it through a FastAPI endpoint with tests and logging.
Is this course for software developers or non-programmers?+
This course is for people who already write Python code — software developers, backend developers, QA automation engineers, data analysts moving into AI engineering, students with basic Python knowledge, and working professionals who know Python basics but cannot yet build APIs.
Prepare for Production AI Engineering
Request the syllabus, or tell us about your Python background and goals — we will confirm whether this bridge course or Production AI Engineering directly is the right starting point for you.