College students who know Python
Turn Python fundamentals into practical API skills for coursework and personal projects.
Python API development · Live mentorship
Learn FastAPI with Python: From Fundamentals to Building APIs
Build Python APIs through live guided coding, practical examples, and project mentorship. Learn the fundamentals, validate data with Pydantic, and understand how APIs can support backend and AI applications.
Live one-to-one learning20–25 session roadmapHands-on coding and doubt clearingBackend and AI project applications
From Python code to an API
- 1A client sends a request
- 2FastAPI validates the data
- 3Your Python logic runs
- 4The API returns a response
A practical path from a Python function to endpoints other applications can use.
Who this is for
A guided next step for Python learners
Python learners new to backend development
Understand how a client talks to a server and build your first endpoints step by step.
Working professionals
Develop API skills through live practice paced around your current Python knowledge.
Python developers exploring AI
Learn how a Python API can provide a useful interface to an existing machine learning or GenAI function.
Before you start
Python basics make FastAPI easier to learn
You should be comfortable with variables, data types, conditions, loops, functions and parameters, lists, dictionaries, and basic modules and imports. Basic JSON familiarity is helpful; we can introduce it along the way. You do not need previous FastAPI experience.
If Python fundamentals still feel unfamiliar, build those first. Complete programming beginners will benefit from learning the foundations before taking on API development.
Explore Python training for beginnersWhat is FastAPI?
A simple way to make Python logic available to other apps
An API is a way for software to request data or an action from another program. A REST API organizes those requests around resources and URLs called endpoints. GET reads data, POST creates it, PUT or PATCH updates it, and DELETE removes it.
FastAPI uses Python type hints and Pydantic models to check incoming data and describe responses. It also creates interactive API documentation automatically, so you can try endpoints in your browser while learning.
Course roadmap
A practical FastAPI and Pydantic curriculum
The outline below plans for about 24 one-hour sessions, within a 20–25 session range. Final session count, depth, and pacing can change to fit your starting level and goals. Some advanced topics may need more practice or additional sessions.
Module 1 · API and HTTP fundamentals · Sessions 1–2
- What APIs are and how clients and servers communicate
- REST APIs, endpoints, requests, and responses
- JSON basics, HTTP methods, and common status codes
Module 2 · FastAPI fundamentals · Sessions 3–6
- Set up a Python environment, FastAPI, and an ASGI server
- Create an application and define routes and path operations
- Use path parameters, query parameters, and JSON responses
- Explore the automatic interactive API documentation
Module 3 · Request data and Pydantic · Sessions 7–10
- Use Python type hints in API code
- Define Pydantic models for request bodies
- Work with required and optional fields, types, constraints, nested models, and lists
- Understand validation errors and response models
Module 4 · Building useful APIs · Sessions 11–14
- Build GET, POST, PUT, PATCH, and DELETE operations
- Plan CRUD endpoints and handle missing resources with HTTP errors
- Organize routes with APIRouter and reusable modules
- Introduction to dependency injection and separating API from application logic
Module 5 · Database integration · Sessions 15–17
- Understand how an API reads and writes database data
- Choose one suitable Python database library for the project
- Implement basic CRUD, configuration, connection handling, and common database errors
Module 6 · Security and production fundamentals · Sessions 18–20
- Use environment variables for configuration and keep secrets out of source code
- Understand authentication, OAuth2 concepts, and bearer tokens
- Discuss password hashing and safe credential handling
- Cover CORS, basic error handling, logging, and endpoint testing
Module 7 · Practical project and integration · Sessions 21–22
- Design endpoints for a realistic application
- Build and test CRUD endpoints with Pydantic input and output models
- Connect the API to a database and introduce serving or deployment if time allows
Module 8 · FastAPI for AI and GenAI · Sessions 23–24
- Create an endpoint that accepts a question and calls an existing Python function
- Validate AI application requests with Pydantic
- Introduce exposing an existing LLM or RAG pipeline and returning appropriate errors
Practice by building
Project ideas for applying the concepts
Core guided practice
- Student management CRUD API
- Task management API
- API with Pydantic request and response models
- Database-backed CRUD application
Optional advanced extension
A basic chatbot or RAG API that connects FastAPI to an existing Python pipeline. This is an introduction to integration, not a promise to master LLM or RAG development in the core sessions. It can require separate AI training, API credentials, and additional time.
How learning works
Live guidance, with time to understand the code
- Live one-to-one guidance and step-by-step explanations
- Write and run code during class, not only watch demonstrations
- Clear doubts before moving on to the next concept
- Practice with exercises and develop a project with guidance
- Adjust the pace to the learner’s understanding
Have broader learning goals? See Python training for working professionals, Generative AI training, or RAG development training.
FAQs
Questions about FastAPI training
What is FastAPI used for?
FastAPI is a Python framework for building web APIs. An API lets another program send a request and receive data or an action in response—for example, a website asking a Python service for a list of tasks.
Do I need to know Python before learning FastAPI?
Yes, basic Python is strongly recommended: variables, conditions, loops, functions, lists, and dictionaries. If you are not comfortable with these yet, start with our Python training first. Prior FastAPI experience is not needed.
Is Pydantic included in the training?
Yes. You will use Pydantic models to describe, validate, and shape the data an API receives and returns.
How many sessions are required?
The proposed roadmap is about 24 one-hour sessions, within a 20–25 session range. The final number and pace depend on your Python level, practice time, and goals.
Can college students join?
Yes. College students with Python fundamentals can learn API development through guided exercises and practical project work.
Can working professionals join?
Yes. The training can be paced around your existing Python knowledge and the API skills you want to develop.
Can I learn to build a REST API?
Yes. The roadmap covers REST concepts, HTTP methods, request and response data, CRUD endpoints, and testing your API.
Is database integration included?
Yes. The course introduces one suitable database approach and uses it for basic CRUD operations. The database choice and depth can be agreed based on your goals.
Can I expose an AI or RAG application through FastAPI?
The roadmap introduces calling an existing Python or AI function from an API endpoint. A complete LLM or RAG integration is an optional extension and may need a separate AI course, API credentials, and extra sessions.
Can the pace be customized?
Yes. The session count and pace can be adjusted after discussing your current level and learning requirements.
How can I enquire about fees and a demo?
Use the WhatsApp enquiry button to ask about prerequisites, fees, session structure, and demo options. The exact schedule and scope can be discussed before enrolment.
Want to Learn FastAPI by Building Real Python APIs?
Enquire about prerequisites, session structure, fees, demo options, and a roadmap suited to your goals. We can discuss the exact scope and schedule before enrolment.