Building intelligent applications using Python, RAG, LLMs, and Computer Vision while exploring scalable AI solutions.
🎓 Computer Engineering Student at Darshan University
💡 I enjoy building AI-powered applications that solve practical problems using Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Computer Vision.
🌱 Currently exploring:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Deep Learning
- MLOps
- Computer Vision
🎯 Goal: Secure an AI/ML Internship and contribute to impactful real-world AI products.
Libraries & Frameworks
- LangChain
- ChromaDB
- Sentence Transformers
- OpenCV
- YOLO
- Groq API
Tech: Python • LangChain • ChromaDB • Streamlit • Groq • Sentence Transformers
An AI-powered chatbot that answers placement-related questions from uploaded PDFs using Retrieval-Augmented Generation.
Highlights
- Semantic Search
- Context-aware Responses
- Vector Database
- Interactive Chat Interface
Tech: Python • LangChain • ChromaDB • Streamlit • Groq
Chat with any YouTube video using transcript extraction, embeddings, semantic search, and LLM-powered responses.
Highlights
- Automatic Transcript Extraction
- Vector Search
- RAG Pipeline
- Fast AI Responses
Tech: Python • YOLO • OpenCV • Streamlit
Detects abnormal crowd activities in real time using computer vision and object detection.
Highlights
- Real-Time Detection
- Interactive Dashboard
- AI-based Monitoring
Tech: MERN Stack
Movie booking platform with authentication, admin dashboard, and booking management.
Tech: Spring Boot • MySQL
REST-based employee management system with CRUD operations and database integration.
- 🤖 Large Language Models (LLMs)
- 📚 Retrieval-Augmented Generation (RAG)
- 🧠 Deep Learning
- ⚙️ MLOps
- ☁️ Docker & Deployment
- Build production-ready AI applications
- Contribute to Open Source
- Learn advanced Deep Learning
- Master MLOps
- Secure an AI/ML Internship