Data Science & AI Engineering Enthusiast | ML Engineer in the Making
📍 Bangalore, India | 🎓 Student | 🚀 Building AI Solutions

"When I'm not coding, I'm either in deep conversations about fascinating topics or exploring new media concepts through photography, storytelling, anime, and manga."

I'm a passionate student transitioning into Data Science and AI Engineering, with a strong focus on building practical, deployable machine learning solutions. I love working with complex datasets—whether it's time series data, social networks, or recommendation systems—and turning them into actionable insights.

My philosophy: Learn deeply, code cleanly, and deploy responsibly.

• 🤖 Specializing in MLOps, Computer Vision, Recommendation Engines, and NLP/LLM
• 🔍 Currently diving into OCR, NLP, and LLM applications
• 🌐 Passionate about AI in daily life and real-world implementation
• 🤝 Open to open-source contributions and collaborations
• 📚 Continuous learner with focus on ML/DL fundamentals

• Python (Primary)
• SQL (Data querying & analysis)
• FastAPI (API development & deployment)

• TensorFlow | Keras (Deep Learning)
• Pandas | NumPy | Scikit-learn (Data processing & ML)
• FAISS (Similarity search & clustering)
• OpenCV (Computer Vision)
• Tesseract (OCR)

• Git & GitHub (Version control)
• Docker (Containerization)
• VS Code (Development environment)

• Time Series Analysis - Forecasting and pattern recognition
• Social Network Data - Analysis across multiple industries
• Movie Recommendation Engine - Fully deployable end-to-end system
• OCR & Text Recognition - Document processing at scale
• NLP & LLM Applications - Natural language understanding and generation

• ML/DL fundamentals (rigorous revision)
• Advanced OCR techniques
• NLP & Large Language Models
• Open-source contribution best practices

Check out my latest work on my GitHub:

A fully deployable recommendation system combining collaborative filtering and content-based approaches.

• Tech: Python, FastAPI, Machine Learning, Docker
• Highlights: Production-ready, scalable architecture
• 🔗 Repository

Deep learning model for recognizing facial emotions in real-time.

• Tech: TensorFlow, Keras, OpenCV, CNN
• Status: Published research backup
• 🔗 FER-CNN-model

• 🤖 Machine Learning Operations - Model deployment, monitoring, versioning
• 👁️ Computer Vision - Image recognition, object detection, facial analysis
• 📄 OCR & Document Processing - Text extraction and understanding
• 🎯 Recommendation Systems - Personalization at scale
• 🗣️ NLP & LLMs - Language understanding and generation
• 🌍 AI in Real Life - Practical implementations that matter

When I'm not immersed in data and algorithms, you'll find me:

📸 Photography - Capturing moments and perspectives
📖 Storytelling - Crafting narratives that resonate
🎌 Anime & Manga - Exploring Japanese storytelling
🎬 Media Concepts - Understanding how stories are told
💬 Deep Conversations - Exploring fascinating ideas and philosophies

I'm always interested in:

• Collaborations on ML/AI projects
• Discussions about data science trends and best practices
• Open-source contributions and community involvement
• Learning opportunities - teaching and being taught

• 💼 LinkedIn: Nikhil Khatri
• 📧 Email: khatrinikhil303@gmail.com
• 💻 GitHub: illusion2600

Currently navigating the journey from solid fundamentals to real-world AI solutions. Every project is a stepping stone toward mastery.

• 🎯 Goal: Become a skilled Data Scientist/AI Engineer
• 🏆 Passion: Building deployable ML systems that solve real problems
• 🧠 Mindset: Always learning, always shipping
• ☕ Fuel: Coffee, curiosity, and challenging problems
• 🌱 Growth: Contributing to open-source and the AI community

• 🎬 Built a fully deployable Movie Recommendation Engine
• 😊 Developed Facial Emotion Recognition CNN model
• 📊 Analyzing time series and social network data across industries
• 🔍 Deep diving into OCR and NLP applications
• 🚀 Contributing to open-source projects and best practices

• 🤖 Internships/Opportunities in ML/AI, Data Science
• 🤝 Collaborations on interesting projects
• 📚 Mentorship in advanced AI/ML topics
• 🌟 Open-source projects to contribute to

1. Learn Deeply - Understand fundamentals, not just frameworks
2. Code Cleanly - Write maintainable, well-documented code
3. Deploy Responsibly - Consider ethics, scalability, and real-world impact
4. Share Knowledge - Contribute back to the community
5. Iterate Continuously - Feedback loops drive improvement

Last Updated: 2026-03-06
Actively learning and growing every single day 🚀