Python AI Course - Organized Learning Projects

A comprehensive collection of Python learning projects organized into full-featured, phased project folders. Each project is self-contained with its own dependencies, documentation, tests, and examples.

🎯 Project Overview

This repository has been reorganized to provide structured learning experiences across different aspects of Python programming, from basic algorithms to advanced web applications and AI implementations.

🚀 Quick Start

Start with AI Development (Recommended) 🤖

cd ai-development-project
pip install -r requirements.txt

# Option 1: Local AI (Recommended for learning)
curl -fsSL https://ollama.ai/install.sh | sh
ollama serve
ollama pull llama3.1:8b

# Option 2: API-based AI
export OPENAI_API_KEY="your-key-here"

# Run first example
python examples/01_simple_llm/basic_chat.py

Or Choose Any Project

# Navigate to any project
cd [project-name]
pip install -r requirements.txt

# Run examples or tests
python main.py          # If available
python -m pytest tests/ # Run tests

📁 Project Structure

Core Learning Projects

🔄 Sorting Algorithms Project

Complete implementations of fundamental sorting algorithms with visualizations.

• Algorithms: Bubble, Selection, Insertion, Merge, Quick, Radix Sort
• Features: Interactive animations, comprehensive tests, performance comparisons
• Tech Stack: Python, Matplotlib, NumPy

🌐 Web Applications Project

Full-stack web applications demonstrating Flask and FastAPI frameworks.

• Flask Apps: Keyword processing, Elasticsearch integration
• FastAPI: Complete blog API with SQLAlchemy ORM
• Features: RESTful APIs, database integration, interactive documentation

📊 Algorithms and Data Structures

Classic computer science algorithms with academic-level documentation.

• Algorithms: Dijkstra's shortest path algorithm
• Features: Graph implementations, LaTeX documentation, comprehensive testing
• Educational: Perfect for CS education and interview preparation

🏭 Design Patterns Project

Object-oriented design pattern implementations with real-world examples.

• Patterns: Factory pattern (with more planned)
• Features: Employee management system, comprehensive examples
• Educational: SOLID principles, OOP best practices

Advanced Specialized Projects

🤖 AI Development Project ⭐ NEW

Comprehensive AI development learning project with practical examples.

• Features: LLMs, Prompt Engineering, RAG, Vector Databases, AI Agents
• Complexity Levels: 🟢 Beginner → 🔴 Expert (4 progressive levels)
• Learning Path: Structured 12-week progression from basics to autonomous agents
• Tech Stack: OpenAI/Ollama, ChromaDB, FastAPI, Vector embeddings
• Examples: 35,000+ word guide with working code for all major AI concepts

💰 NASDAQ CSE Trading Simulator (Python)

Professional-grade trading simulator with AI assistance.

• Features: Real-time trading, AI bot, risk management, FIX/FAST protocols
• Tech Stack: FastAPI, SQLAlchemy, WebSocket, Scikit-learn

🚀 NASDAQ CSE Trading Simulator - Go

High-performance Go implementation of the trading simulator.

• Features: Enhanced performance, concurrent processing, native Go implementation
• Tech Stack: Go, Gin, GORM, Gorilla WebSocket

📈 Bayesian Statistics & AI Tools

Advanced statistical analysis and AI interview preparation tools.

• Features: Bayesian market analysis, AI interview questions trainer
• Tech Stack: PyMC3, Tkinter, Statistical analysis

✈️ AI Flight Tracker

Flight tracking application with AI capabilities.

🤖 AI Generation Tools

AI-powered content generation utilities.

🚀 Quick Start

Each project is self-contained. Navigate to any project directory and follow its README:

# Example: Running the sorting algorithms project
cd sorting-algorithms-project
pip install -r requirements.txt
python src/basic_sorting.py

# Example: Running the web applications
cd web-applications-project
pip install -r requirements.txt
cd flask-app && python keyword_processor.py

🎓 Educational Value

This repository provides:

• Structured Learning: Progress from basic algorithms to complex applications
• Best Practices: Proper project organization, testing, documentation
• Real-World Examples: Practical applications of theoretical concepts
• Multiple Paradigms: Procedural, OOP, functional programming patterns
• Technology Diversity: Web frameworks, databases, AI/ML, algorithms

📋 Project Comparison

Project	Language	Complexity	Focus Area	Key Technologies
AI Development	Python	🟢→🔴 Progressive	AI Development	LLMs, RAG, Vector DBs, Agents
Sorting Algorithms	Python	Beginner	Algorithms	Matplotlib, NumPy
Web Applications	Python	Intermediate	Web Development	Flask, FastAPI, SQLAlchemy
Algorithms & DS	Python	Intermediate	Computer Science	Graph theory, Academic documentation
Design Patterns	Python	Intermediate	Software Design	OOP, SOLID principles
NASDAQ CSE	Python	Advanced	Financial Technology	Trading, AI, Real-time systems
NASDAQ CSE Go	Go	Advanced	System Programming	High performance, Concurrency
Bayesian Stats	Python	Advanced	Data Science	Statistical analysis, ML

🔧 Development Setup

Prerequisites

• Python 3.7+
• Go 1.19+ (for Go projects)
• Git

Installation

git clone https://github.com/smaruf/python-ai-course.git
cd python-ai-course

# Navigate to any project and install its dependencies
cd [project-name]
pip install -r requirements.txt

🧪 Testing

Each project includes comprehensive tests:

# Run tests for any project
cd [project-name]
python -m pytest tests/ -v

📚 Learning Path

Recommended progression:

1. AI Development → Start here! Learn modern AI development from basics to advanced
2. Sorting Algorithms → Learn basic algorithm concepts and programming fundamentals
3. Design Patterns → Understand OOP and software design principles
4. Algorithms & Data Structures → Advanced CS concepts and graph theory
5. Web Applications → Full-stack development with modern frameworks
6. NASDAQ CSE → Complex system integration with real-time trading
7. Bayesian Stats → Advanced data science and statistical AI

🎯 Quick Start Recommendations

New to Programming? → Start with Sorting Algorithms Project New to AI? → Start with AI Development Project (🟢 Beginner level) Experienced Developer? → Jump to AI Development Project (🟡 Intermediate level) Want Full-Stack Skills? → Try Web Applications Project Interest in Finance/Trading? → Explore NASDAQ CSE Project 6. Bayesian Stats → Advanced data science and AI

🤝 Contributing

Each project welcomes contributions:

• Bug fixes and improvements
• New algorithms and patterns
• Enhanced documentation
• Additional test cases

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.