Python for AI & FastAPI: Complete Course Route Map

Navigate the complete Python for AI & FastAPI course syllabus — every module, topic, and sub-topic with direct links to all learning materials.

Welcome to the Python for AI & FastAPI course route map. Use this page as your interactive table of contents and navigation guide. Every module, topic, and sub-topic below links directly to its learning material. Whether you are following the course from start to finish or jumping to a specific concept, this is your starting point.

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Course structure overview

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Module 1: Getting Started

Setting up your development environment, learning the editor, and understanding how Python projects are structured and managed.

1.1 Python Setup

• What is Python? — Dynamic typing, the interpreter, and why Python dominates AI
• Installing Python — Overview with links to platform guides
• Windows Installation — Step-by-step guide for Windows
• macOS Installation — Step-by-step guide for macOS
• Linux Installation — Step-by-step guide for Linux (Ubuntu, Fedora, Arch)

1.2 VS Code and the Python Editor

• VS Code Introduction — Why VS Code is the standard IDE for Python and AI development
• VS Code Setup — Installing VS Code, Python, Pylance, and Jupyter extensions
• VS Code Workspace — Creating project folders, workspaces, and understanding the interface
• First Python File — Writing and running your first .py script
• Jupyter Notebooks — Creating and running .ipynb files in VS Code and Colab
• Ruff Linter — Automatic code formatting and linting on save

1.3 Python Environments and Packages

• Virtual Environments — Why isolation matters and how to create a .venv per project
• pip & Packages — Installing packages from PyPI, managing requirements.txt
• Interactive Python REPL — Using the REPL and VS Code’s interactive window with Shift + Enter

1.4 Modern Tooling with uv

• uv Overview — What uv is and why it replaces the traditional pip + venv toolchain
• Why uv? — Speed comparison, feature breakdown, and installation
• Virtual Envs with uv — Creating projects and managing packages with uv
• Complete Project Setup — End-to-end guide: uv + VS Code + Git + GitHub

1.5 Course Materials

• Course Resources — Practice notebooks, exercise files, and real-world datasets

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Module 2: Python Basics

Core Python syntax, data types, control flow, data structures, and the foundational building blocks of every Python program.

2.1 Core Syntax & Data Structures

• Python Basics — Variables, operators, conditionals, loops, and control flow
• Data Structures — Lists, tuples, dictionaries, and sets
• Packing & Unpacking — *args, **kwargs, and sequence unpacking

2.2 Functions and Modules

• Functions — Defining functions, parameters, return values, and scope
• Modules & Packages — Custom module files and __init__.py

2.3 OOP and Error Handling

• Classes & OOP — Classes, instances, self, inheritance, and encapsulation
• Exception Handling — try/except/finally and custom exceptions

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Module 3: Advanced Python

Language internals, functional programming patterns, type validation with Pydantic, and working with external libraries.

3.1 Advanced Concepts

• Python Internals — CPython memory management and reference counting
• Comprehensions — List, dictionary, set comprehensions, and generators
• Advanced Functions — Closures, first-class functions, and decorators
• Functional Programming — map(), filter(), and reduce()
• Advanced OOP — Abstract Base Classes, metaclasses, and dunder methods
• Context Managers — The with statement and custom context managers
• Working with Data — Files, JSON, and CSV in Python
• dotenv — Managing secrets with .env files
• Async Programming — async/await, coroutines, and asyncio
• Dependency Injection — Decoupling code with DI patterns
• Type Hints — Annotating Python code for safety and tooling
• Dataclasses — Clean data models using @dataclass

3.2 Pydantic and Data Validation

• Introduction to Pydantic — Runtime validation using type hints
• Your First Model — Creating BaseModel subclasses
• Validation & Fields — Field constraints and custom validators
• Nested Models — Composing Pydantic models together
• Pydantic Settings — Type-safe configuration from environment variables

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Module 4: Developing Projects

Structuring production-grade Python applications, managing imports, and building complete real-world programs.

4.1 Developer Tools

• Tools Overview — Git, environment variables, and code quality tools
• Version Control — Core Git concepts and the basic commit workflow
• GitHub Setup — Creating an account and authenticating
• Clone & Create — Cloning repos and publishing new projects
• Git in VS Code — Managing Git through the Source Control panel

4.2 Environment & Secrets

• Environment Variables — os.environ patterns and use cases
• .env Files — python-dotenv setup and .env.example conventions

4.3 Project Structure

• Project Structure — Standard folder layouts and file organisation
• Python Paths — sys.path, imports, and the -m flag
• Organising Code — Refactoring scripts into reusable modules
• Project Handling — Modules, packages, and virtual environments

4.4 Weather Data Project

• Project Overview — Introduction to the hands-on project
• uv Introduction — Setting up the project environment with uv
• Virtual Env Setup — Creating and activating environments
• Complete Setup — Full end-to-end project configuration
• Data Analysis Project — Fetch, process, and visualise weather data

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Module 5: Data Analysis & Databases

The mathematical and data manipulation foundations for machine learning and AI.

5.1 SQL & Databases

• SQL Introduction — Databases, DBMS, and relational concepts
• DDL & DML — Creating tables and modifying data
• SQLite Setup — Setting up your practice database
• Querying with SELECT — Filtering, sorting, and aggregating data
• SQL Joins — Combining data from multiple tables
• Advanced SQL — Normalisation, ACID, and window functions

5.2 NumPy

• NumPy Arrays — NDArrays, slicing, indexing, and broadcasting

5.3 Pandas

• Pandas DataFrames — Data wrangling, missing values, and aggregation

5.4 Data Visualisation

• Matplotlib — Line plots, scatter plots, bar charts, and subplots
• Seaborn — Statistical plots, heatmaps, and distribution charts
• Visualisation Guide — End-to-end dataset plotting pipelines

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Module 6: FastAPI

Building production-ready REST APIs with Python.

6.1 Foundations

• FastAPI Overview — What you’ll build and course module overview
• How the Web Works — HTTP, client-server, and REST concepts
• Intro to FastAPI — Installation, features, and your first app
• FastAPI Fundamentals — Routing, path params, query params, request bodies
• Request Handling — Path/query/body/header inputs with Annotated
• Response Handling — Response models, status codes, and HTTPException
• Data Validation Models — Request, internal, and response model patterns
• CRUD Operations — Full in-memory CRUD API implementation
• API Documentation — Swagger UI, ReDoc, and app metadata

6.2 Advanced FastAPI

• APIRouter — Modular routing for larger applications
• Exception Handling — Custom error handlers and HTTP exceptions
• Dependency Injection — Depends(), yield sessions, and shared logic
• Modularisation — Refactoring into routers, services, and schemas
• SQL & ORM — SQLModel, sessions, and persistent CRUD
• JWT Authentication — Token-based auth, protected routes, and RBAC

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Module 7: Frontend with Streamlit

Building interactive data applications and dashboards in pure Python — no JavaScript required.

• Introduction to Streamlit — App architecture and getting started
• Displaying Content — Text, markdown, dataframes, and charts
• Widgets — Buttons, inputs, sliders, and file uploaders
• Data Handling — Reading CSV and JSON, uploading files
• Building a Full App — Complete student dashboard project