Create Python AI Engineering course repository structure with 12 pillars

[Copilot]

This PR establishes the complete structure for the Python AI Engineering course repository, creating a comprehensive learning framework organized into 12 distinct pillars.

Changes Made

Core Files

• README.md: Updated with comprehensive course overview, including course objectives, structure description, installation instructions, and dependency documentation
• requirements.txt: Added all required Python packages for the course:
  • numpy (numerical computing)
  • pandas (data manipulation)
  • pytest (testing framework)
  • mypy (static type checking)
  • pydantic (data validation)
  • fastapi (API development)

Course Structure

Created 12 pillar directories (pillar-01 through pillar-12), each representing a key aspect of Python AI engineering:

1. Pillar 01: Python Fundamentals
2. Pillar 02: Data Structures and Algorithms
3. Pillar 03: Object-Oriented Programming
4. Pillar 04: Functional Programming
5. Pillar 05: Data Processing with NumPy and Pandas
6. Pillar 06: Testing with Pytest
7. Pillar 07: Type Checking with MyPy
8. Pillar 08: Data Validation with Pydantic
9. Pillar 09: API Development with FastAPI
10. Pillar 10: Machine Learning Basics
11. Pillar 11: Deep Learning Fundamentals
12. Pillar 12: AI Engineering Best Practices

Each pillar includes:

• examples/ - Practical code demonstrations
• labs/ - Hands-on exercises
• project/ - Comprehensive pillar project
• solutions/ - Solutions to exercises
• README.md - Detailed pillar-specific overview with learning objectives

Repository Organization

The repository now provides a clear learning path from Python fundamentals through advanced AI engineering concepts, with dedicated spaces for examples, practice exercises, projects, and solutions in each pillar. The structure supports progressive learning while maintaining consistency across all modules.

The existing .gitignore (Python) and LICENSE (MIT) files remain unchanged and provide proper version control and licensing for the course materials.

Original prompt

Create Python course repo "python-ai-engineering" with:

• README.md with course overview
• requirements.txt (numpy, pandas, pytest, mypy, pydantic, fastapi)
• .gitignore for Python
• LICENSE (MIT)
• 12 folders: pillar-01 through pillar-12
• Each folder has: examples/, labs/, project/, solutions/, README.md

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