Atiatunnasir Arike Oni-Bashir

AI/ML developer skilled in constructing and evaluating deep learning models using Python, TensorFlow, and Scikit-learn. Experienced in working on projects like CovDetect (medical imaging), car price prediction (regression), and AgroScan (CV-based disease detection with 98% accuracy). Proficient in data preprocessing, model tuning, and implementing ML solutions using platforms such as Streamlit and Hugging Face.

AgroScan:

CNN-based leaf disease detection model deployed via WhatsApp using Twilio and Hugging Face. Built as part of the Africa Deep Tech Challenge.

MATAI:

Maternal health risk classifier that outputs high, medium, or low risk based on antenatal data. Built for HelpMum Hackathon. Multilingual interface and Gradio UI in progress.

CovDetect:

Chest X-ray classification model distinguishing between COVID-19 and normal lungs. Utilizes both a custom CNN and pretrained ResNet architecture. Trained on the COVID-19 Radiography Dataset and built with TensorFlow.

Car Price Predictor:

Linear regression model predicting car prices based on mileage, year, fuel type, and engine size. Evaluated with RMSE. Visualized using Seaborn and Matplotlib.

• Data Science & Machine Learning – 3MTT DeepTech Ready Programme (In Progress)
• Data Analysis – MKAN Nigeria (In Progress)
• AI/ML Fundamentals – Transformation College (In Progress)
• Data Science for Precision Medicine – Stanford Data Ocean (In Progress)
• Introduction to R Programming – AFREDAC
• Cloud and Cybersecurity – Panoramic Synergy Enterprise
• Cybersecurity – Wootlab Foundation
• Research Training on AI – FAMSA SCOMER / CIIRNET
• Identifying Research Gaps & Publishing – FAMSA SCOMER
• National Healthcare Interprofessional Exchange Program (Oct 2024)
• World Pre-Health Conference (Mar 2025)