Python for AI & Data

Repository Overview

This repository contains a curated set of Jupyter notebooks designed to build a strong Python foundation for data and AI work. It is structured to learn and progress from core language skills to advanced topics used in modern AI engineering:

• decorators and advanced function patterns
• pydantic for validation and data modeling
• asyncio for asynchronous programming
• ai-agents building using plain Python without external frameworks

The notebooks focus on practical examples and runnable code so we can explore and adapt the patterns for Data and AI use cases.

Learning Path Diagram

[Decorators] --> [Pydantic] --> [Async Python] --> [AI Agents]
      |                |               |                 |                 
      |                |               |                 |               
   Better            Strong          Concurrent       Framework-
abstractions        validation       executions      free AI logic

Setup Instructions

1. Clone the repository:

git clone https://github.com/pasrichashivam/python-for-ai-data.git
cd python-for-ai-data

2. Sync the project with uv if available in your environment:

uv sync

If uv is not available, use your preferred package manager.

3. Install dependencies required by a notebook environment. If you are using a Python virtual environment:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Repository Structure

Module	Description
01-decorators	Decorator patterns, higher-order functions, and practical uses in data pipelines and AI
02-pydantic_models	Data validation, typed models, settings, and best practices using pydantic with AI
03-async_runs	Step bt step progess with Async programming with asyncio, concurrency patterns
04-agent_building	Building simple AI Agentic loop using bare Python logic, without external frameworks

Learning Path

The repository follows a step-by-step learning path:

1. Master decorators to build reusable abstractions.
2. Use pydantic to validate and structure data.
3. Add async programming skills for scalable API calls, IO and workflows.
4. Build AI agent logic with bare Python, focusing on design rather than framework usage.

How to Use the Notebooks

• Open each notebook sequentially to follow the learning flow.
• Run the examples and modify code blocks to experiment with behavior.
• Use the notebooks as templates for new AI and data projects.
• Combine patterns from multiple notebooks to build more advanced applications.

Notes

• This repository is ideal for learners who want to move beyond libraries and understand the Python foundations underpinning modern AI systems.
• The emphasis is on simple, practical examples that can be reused in real projects.