Portfolio notes and coursework from MIT Professional Education: Applied Data Science - Leveraging AI for Effective Decision-Making.
This repository documents applied machine learning, statistical reasoning, data visualization, supervised and unsupervised learning, neural networks, recommendation systems, time-series analysis, and computer vision practice completed through the program.
My main professional focus is bioinformatics and computational biology. This program strengthened the AI/ML layer of that work: how to frame prediction problems, evaluate models, avoid overclaiming, and communicate model behavior clearly to scientific and business stakeholders.
The capstone project focused on computer vision for biomedical imaging, using microscopy/RBC images for malaria detection.
Highlights:
- Built preprocessing workflows with OpenCV.
- Implemented CNN architectures with TensorFlow/Keras.
- Compared custom CNN performance against transfer-learning baselines.
- Achieved 97% accuracy and 99% sensitivity in the project setting.
- Emphasized sensitivity because missed positive cases are especially costly in screening contexts.
| Area | Examples |
|---|---|
| Programming | Python, Jupyter notebooks, pandas, NumPy |
| Machine learning | Regression, classification, clustering, model evaluation |
| Deep learning | Neural networks, CNNs, TensorFlow, Keras |
| Computer vision | OpenCV preprocessing, image classification |
| Communication | Case-study writeups, model interpretation, result summaries |
The same modeling discipline used here applies directly to omics and translational research:
- Define the biological or clinical question before modeling.
- Separate signal from leakage, confounding, and batch effects.
- Choose metrics that match the scientific risk.
- Treat model output as evidence, not truth.
- Make workflows reproducible enough for another scientist to inspect.
Program: MIT Professional Education - Applied Data Science: Leveraging AI for Effective Decision-Making.
This repository contains personal coursework notes and project artifacts. MIT and course instructors deserve credit for the program structure and teaching material; interpretations and portfolio framing here are my own.