I build and experiment with machine learning systems, with a focus on turning ideas into practical, reproducible solutions.
My work sits at the intersection of Data Science, Deep Learning, Computer Vision, NLP, and MLOps.
Machine Learning ███████████████████░░
Deep Learning ██████████████████░░
Computer Vision ████████████████░░░░░
NLP / Transformers ███████████████░░░░░
MLOps █████████████░░░░░░░
Data Analysis █████████████████░░░
Interested in building ML systems that are not only accurate, but also reproducible, interpretable, and practical.
Languages
Python C C++ SQL
Machine Learning
Scikit-learn Pandas NumPy SHAP
Deep Learning
PyTorch TensorFlow Keras CNNs Transformers
Computer Vision & NLP
OpenCV BERT ViT Multimodal Learning
MLOps & Experimentation
DVC MLflow DagsHub Git Linux
Deployment
Streamlit Flask
Deep Learning + MLOps
A computer vision system for chicken disease classification using VGG16 transfer learning, combined with DVC for reproducible ML pipelines.
Machine Learning + Experiment Tracking
An end-to-end regression project comparing 8 ML algorithms, with experiment tracking through MLflow + DagsHub and model interpretation using SHAP.
Machine Learning:
- Predictive Modeling
- Model Optimization
- Model Evaluation
- Explainable AI
Deep Learning:
- Computer Vision
- NLP
- Transformers
- Multimodal Learning
ML Engineering:
- Reproducible Experiments
- MLOps
- Experiment Tracking
- Deployment01 — Understand
Start with the problem, data, and assumptions.
02 — Experiment
Build baselines, test architectures, and compare results.
03 — Analyze
Look beyond accuracy — understand errors, robustness, and model behavior.
04 — Reproduce
Use proper experiment tracking and reproducible pipelines.
05 — Deploy
Turn promising experiments into usable applications.
M.Tech — Computer Science (Data Science)
Sardar Vallabhbhai National Institute of Technology, Surat
B.Tech — Computer Science & Engineering
Deenbandhu Chhotu Ram University of Science and Technology, Murthal
→ Exploring new ML architectures
→ Building practical AI projects
→ Working with multimodal learning
→ Improving ML engineering & MLOps skills
→ Learning something new every day
I enjoy taking an ML idea from a notebook experiment → a working system.
If you're interested in Machine Learning, Deep Learning, Data Science, or AI research, feel free to explore my repositories.
Build. Experiment. Learn. Repeat.
⭐ If you find something useful here, consider starring the repository.