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project_name: personal_learning_lab
github_url: https://github.com/neural-arun/personal_learning_lab
language: HTML
stars: 1
topics:
  - None
updated_at: 2026-04-14T14:44:25.000Z

personal_learning_lab

GitHub Repository: https://github.com/neural-arun/personal_learning_lab
Primary Language: HTML | Stars: 1 | Forks: 0
Description: No description provided.

🧪 Personal Learning Lab | Arun Yadav

🚧 Code. Break. Fix. Document.

The central repository for my learning journey.

📖 About This Repo

This is not a portfolio for clients. This is my laboratory.

Here, I document my raw learning process, code snippets, failures, and breakthroughs as I build the technical foundation for NEETPrepGPT and Symptom2Specialist. This repository serves as the "source of truth" for my technical growth, structured strictly according to my Roadmap.

Current Focus: Phase 1 — Building the Core Technical Foundation (Python, Backend, AI, & DevOps).

🗺️ The Roadmap: Phase 1 Execution

My learning is divided into 4 high-performance modules. I do not move to the next module until the Definition of Done is met.

🐍 Module 1: The High-Performance Data Engineer

Theme: "The Python, The Speed, The Hunter, The Analyst"
Goal: transform from script-kiddie to professional engineer capable of high-speed data gathering.

Advanced OOP: Class inheritance, polymorphism, and "Pythonic" blueprints.
Async & Speed: asyncio, await, and non-blocking I/O.
Web Scraping (The Hunt): aiohttp for speed, Playwright for dynamic sites.
Data Science Stack: cleaning messy scraped data with Pandas & NumPy.
Engineering Habits: Git flow and pytest for stability.
🏁 Capstone Project: "The Async Data Pipeline" (Scrape → Clean → Analyze).

🏗️ Module 2: The Backend Monolith

Theme: "The Architect, The Guard, and The Flash"
Goal: Build a secure, scalable API server (The Engine of NEETPrepGPT).

Database Design: PostgreSQL, Normalization, and complex SQL joins.
FastAPI Framework: Pydantic validation, Dependency Injection, REST architecture.
ORM & Migrations: SQLAlchemy & Alembic for database management.
Security: JWT Authentication, Password Hashing (Bcrypt).
Caching: Redis for high-performance data retrieval.
🏁 Capstone Project: "The Secure Backend System" (Auth + API + DB).

🧠 Module 3: The AI Intelligence Layer

Theme: "The Brain, The Memory, and The Reasoning"
Goal: Integrate LLMs to "read" scraped data and answer student queries without hallucinations.

LLM Fundamentals: OpenAI API, Tokens, Temperature, System Prompts.
Prompt Engineering: Chain of Thought, Few-Shot prompting.
Vector Databases: Embeddings, Cosine Similarity, ChromaDB/Pinecone.
RAG Pipeline: Retrieval-Augmented Generation (The core of my product).
Evaluation: Preventing hallucinations and managing API costs.
🏁 Capstone Project: "The AI Tutor Bot" (RAG-powered Q&A).

🚢 Module 4: Interfaces & Deployment

Theme: "The Ship, The Container, and The Cloud"
Goal: Package the logic into a Telegram Bot and deploy via Docker.

Telegram Bot Interface: python-telegram-bot, Webhooks, State Machines.
Dockerization: Containerizing the App, DB, and Redis with docker-compose.
CI/CD: Automating tests and deployment with GitHub Actions.
Cloud Deployment: Hosting the full stack on Railway/Render.
🏁 Capstone Project: "NEETPrepGPT Live" (Production Launch).

🛠️ Tech Stack & Tools

Category	Technology
Language
Backend
Data & AI
Database
DevOps

📂 Repository Structure

personal_learning_lab/
├── 01_advanced_python/      # Module 1: OOP, Decorators, Async experiments
├── 02_scraping_lab/         # Module 1: Scrapers (Requests, Selenium)
├── 03_fastapi_core/         # will be continued.