| Project | Description |
|---|---|
| 🚀 The-Neural-Nexus | Cutting-Edge Deep Learning Lab |
| 🧠 NLP-Navigator | Real-World NLP Applications |
| 🎯 Suggestify-RecommendationSystems | Scalable Recommender Engines |
| 📊 DS-ML-Playground | Diverse ML Problem Solving |
| 🧪 Applied-AI-Lab | End-to-End AI/ML Experimentation |
| ⚙️ PySpark-Pipeline | Big Data ML with PySpark |
| 🍃 MongoDB-Mechanics | NoSQL Data Handling for ML |
| 🐍 100DaysOfCode-Python | Rapid Python Mastery Journey |
| 🛠️ Useful-Code-Snippets | Production-Ready Code Boosters |
- 🎯 Pursuing MTech in Data Science, Machine Learning & Artificial Intelligence
- 🧪 Research focus in Smart Mobility, Computer Vision, and Reinforcement Learning
- 🛠️ Building end-to-end Machine Learning systems
- 🌟 Passionate about clean code and scalable solutions
- Applied Machine Learning & Deep Learning
- NLP, Computer Vision, Time Series, Reinforcement Learning
- Model Deployment, MLOps, CI/CD Pipelines
- Data Engineering with Big Data Tools (Kafka, Spark, Elasticsearch)
- Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost
- DevOps: Docker, Kubernetes, Jenkins
- Cloud: AWS (EC2, S3, SageMaker), GCP (Vertex AI)
- Data: SQL, MongoDB, Airflow, Spark, Kafka
- 📄 Working on Deep Reinforcement Learning, Computer Vision, and Explainable AI projects.
- 🧠 Author of two XAI papers (under review):
- A Concise Survey of Explainable AI (XAI) Techniques – Methods, Applications, and Challenges
- Domain-Aligned Framework for Explainable AI: Matching Techniques to Application Needs (DAX Framework)
- 📌 Exploring hybrid frameworks combining Machine Learning and Operations Research.
- 🧬 Passionate about bridging theory and real-world applications.
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Live Portfolio
