docs(01-python): Add Object-Oriented Programming and Dataclasses practice guide #7

🎯 Goal

Expand Module 1 (01-python/README.md) by adding hands-on practice exercises covering Object-Oriented Programming (OOP), Type Hinting, and Python dataclasses tailored for AI/ML engineering.

📌 Background

Many 1st and 2nd year students learn basic procedural Python (loops, functions, lists) but struggle when encountering modern AI libraries that rely heavily on classes (e.g., PyTorch nn.Module, Scikit-Learn custom transformers, and Pydantic/dataclass data schemas).

📋 Scope and Requirements

1. Target File: 01-python/README.md.
2. Add Section: "OOP and Modern Python Patterns for Machine Learning".
3. Cover with Clean Code Examples:
   • Type Annotations: Function signatures with list[float], dict[str, Any], Optional, and return types.
   • Python @dataclass: Creating clean, immutable data containers (e.g., DatasetConfig, ModelMetrics).
   • OOP Architecture: A mini class hierarchy (e.g., BasePreprocessor and TextPreprocessor implementing .fit() and .transform()).
4. Exercises: 3 small student challenge exercises with an expandable <details><summary>Solution</summary></details> block.

🏷️ Good First Issue

Perfect for students looking to strengthen their Python foundations! Comment below if you would like to be assigned.