class MubashirUlHassan:
def __init__(self):
self.role = "Data Scientist & AI Engineer"
self.focus = ["Generative AI", "LLMs", "Machine Learning"]
self.currently_learning = ["RAG Systems", "Fine-tuning LLMs", "Prompt Engineering"]
self.ask_me_about = ["Data Science", "Python", "ML", "GenAI"]
def get_daily_routine(self):
return ["☕ Coffee", "💻 Code", "🤖 Train Models", "📚 Learn", "🔁 Repeat"]- 🤖 Building AI-powered applications using Large Language Models
- 📊 Creating interactive dashboards for data-driven insights
- 🧪 Experimenting with prompt engineering and RAG systems
- 🚀 Deploying ML models on Hugging Face Spaces
| Project | Description | Tech Stack |
|---|---|---|
| 🐦 Twitter Sentiment Analyzer | NLP app for real-time tweet sentiment analysis | DistilBERT, Gradio, HF |
| 📊 Prompt2SQL | Natural Language to SQL Query Conversion Using Generative AI | Gemini API, Streamlit |
| 🦷 Oral-Disease-RAG-Classifier | Oral disease diagnosis + RAG medicine recommendations | DL, RAG, Gradio |
- Master Generative AI and build production RAG systems
- Contribute to open-source AI projects
- Fine-tune a custom LLM for specific use cases
- Get certified in Google Cloud ML or AWS AI
- Build 5+ AI projects and deploy to production