Python for AI/ML

A structured, hands-on reference repository covering core Python concepts and foundational AI/ML topics. This repository is intended to serve as a learning resource for developers who want to strengthen their Python fundamentals with clear, well-commented examples in both interactive notebooks and standalone scripts.

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Table of Contents

• Overview
• Prerequisites
• Getting Started
• Repository Structure
• Topics Covered
• Running the Notebooks
• Running the Scripts
• License

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Overview

This repository is organized into two primary components:

• notebooks/ — Interactive Jupyter notebooks grouped by topic, ideal for step-by-step exploration.
• src/ — Standalone Python scripts demonstrating concepts in a production-style format.

Each topic is self-contained and can be studied independently.

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Prerequisites

• Python 3.10 or higher
• pip (Python package manager)
• JupyterLab (installed via requirements.txt)

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Getting Started

1. Clone the Repository

git clone https://github.com/aneeb02/python-ai-ml.git
cd python-ai-ml

2. Create and Activate a Virtual Environment

Linux / macOS:

python3 -m venv venv
source venv/bin/activate

Windows:

python -m venv venv
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

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Repository Structure

python-ai-ml/
│
├── notebooks/                  # Interactive Jupyter notebooks
│   ├── 01_python_basics/       # Core syntax, functions, scope, operators
│   ├── 02_data_structures/     # Lists, comprehensions, built-in structures
│   ├── 03_advanced_python/     # OOP, decorators, generators, memory management
│   ├── 04_concurrency/         # Async, multithreading, multiprocessing
│   ├── 05_design_patterns/     # Dependency injection, generator pipelines
│   └── 06_ai-ml-intro/         # NumPy, introductory ML concepts
│
├── src/                        # Standalone Python scripts
│   ├── advanced_python/        # Context managers, descriptors, monkey patching
│   ├── design_patterns/        # Factory pattern, dependency injection
│   ├── ipc/                    # Inter-process communication (pipes, queues, shared memory)
│   └── mini_projects/          # Small self-contained programs (Mastermind, Password Generator)
│
├── data/                       # Sample datasets and input files
├── docs/                       # Reference documents and supplementary reading
├── logs/                       # Runtime log output (not tracked by git)
│
├── requirements.txt            # Python dependencies
├── .gitignore                  # Git ignore rules
└── README.md                   # Project documentation

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Topics Covered

Python Basics

• Variables, operators, and control flow
• Functions, scope, and closures
• Exception handling and file I/O

Data Structures

• Lists, tuples, sets, and dictionaries
• List and dictionary comprehensions
• Stacks, queues, and linked list implementations

Advanced Python

• Object-Oriented Programming: inheritance, dunder methods, descriptors
• Decorators (function and class-based)
• Generators and iterators
• Memory management and garbage collection
• Copy mechanisms: assignment, shallow, and deep copy
• Regular expressions

Concurrency

• asyncio and asynchronous programming
• Multithreading with the threading module
• Multiprocessing with the multiprocessing module
• Inter-Process Communication (IPC): pipes, queues, shared memory

Design Patterns

• Factory Pattern
• Dependency Injection
• Generator Pipelines

AI/ML Introduction

• NumPy arrays and operations
• Introductory machine learning concepts with scikit-learn

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Running the Notebooks

Start JupyterLab from the project root:

jupyter lab

Navigate to the notebooks/ directory in the JupyterLab file browser and open any .ipynb file.

Note: Notebooks that read from files (e.g., file_handling.ipynb, generator_pipeline.ipynb) use relative paths that assume JupyterLab is launched from the project root directory.

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Running the Scripts

Scripts are standalone and can be run directly from the project root:

python3 src/mini_projects/password_gen.py
python3 src/mini_projects/mastermind.py
python3 src/ipc/ipc_shared_memory.py

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Contribute

If you feel anything is missing or some concept can be added or further explained, feel free to contribute!

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