// aspiring ml engineer & builder
Turning raw data into intelligent systems. I design and train machine learning models that solve real-world problems — from vision to language to structured predictions.
01 / About
I'm an aspiring ML engineer with a deep passion for building systems that learn, adapt, and predict. My work spans computer vision, NLP, and classical ML — always with a focus on clean experiments and reproducible results.
I believe great ML isn't just about accuracy metrics. It's about understanding the data deeply, designing thoughtful architectures, and communicating results clearly.
Currently exploring the intersection of large language models and practical applications, while sharpening my fundamentals in mathematics and systems thinking.
02 / Models
Fine-tuned ResNet-50 to classify 38 plant disease categories from leaf images. Trained on the PlantVillage dataset with heavy augmentation to handle field-condition noise.
Fine-tuned BERT-base on multi-domain reviews (movies, products, restaurants). Handles nuanced sentiment including sarcasm and negation patterns better than bag-of-words baselines.
End-to-end regression pipeline using XGBoost with extensive feature engineering on the Ames Housing dataset. Includes SHAP explanations for interpretability.
Lightweight real-time mask detection using MobileNetV2 backbone. Optimised for edge inference — runs at 30fps on CPU. Includes face localisation pre-processing.
Multi-label news categorisation using DistilBERT with zero-shot capability for unseen topics. Deployed as a REST API; processes 500+ articles per second.
Churn prediction pipeline for a telecom dataset. Handles severe class imbalance with SMOTE + calibrated probabilities. SHAP waterfall plots explain individual predictions.
03 / Experience
Building skills one experiment at a time.
Independently replicating landmark papers (Attention Is All You Need, ResNets, DDPM). Contributing to open-source ML repositories and participating in Kaggle competitions to stress-test fundamentals.
Completed Andrew Ng's Deep Learning Specialisation and fast.ai. Built 6+ end-to-end projects covering vision, NLP, and tabular ML. Focused on clean code, reproducibility, and experiment tracking with MLflow.
Mastered core Python, NumPy, Pandas, and Matplotlib. Built classical ML models from scratch to understand the mathematics. First Kaggle competition: top 20% on Titanic survival prediction.
04 / Contact
Open to collaborations, internships, research opportunities, and interesting conversations about machine learning.