Results-driven Machine Learning Engineer with hands-on expertise in Computer Vision, Natural Language Processing, and Conversational AI. I specialize in designing and deploying end-to-end machine learning solutions that achieve 76–98% model accuracy across real-world applications.

Recently completed a 6-month industrial training at University College Hospital (UCH), Ibadan, where I developed AI tools for healthcare analytics while working with privacy-sensitive medical data.

Currently pursuing: B.Tech. in Computer Science at Ladoke Akintola University of Technology (LAUTECH) | Expected 2026 | GPA: 4.3/5.0

Programming Languages: Python, SQL

Frameworks & Libraries: TensorFlow, Keras, PyTorch, Scikit-learn, Pandas, NumPy

AI/ML Domains: Computer Vision, NLP, Large Language Models (LLMs), Conversational AI

Deep Learning: CNNs, Transfer Learning, Embeddings, Sequence Modeling

Tools & Platforms: Streamlit, Git/GitHub, Jupyter, Google Colab, Kaggle

Data Science: Data Wrangling, Model Evaluation, Matplotlib, Seaborn

Tech Stack: AutoGen • LLM • Streamlit • Groq API

Production-ready medical chatbot delivering safe, empathetic, multi-turn conversations with patients.

• Integrated Microsoft AutoGen Framework for agentic reasoning and contextual dialogue management
• Implemented emergency-response detection and medical disclaimer protocols for responsible AI behavior
• Deployed Streamlit-based web app with chat history, quick-actions, and intuitive user interface
• Ensures HIPAA-aware conversation flow with safety filters

Tech Stack: NLP • Universal Sentence Encoder • Conv1D • TensorFlow

Multi-label classification system for automating biomedical literature review.

• Achieved 80% accuracy on PubMed abstract classification across multiple categories
• Engineered hybrid architecture combining USE embeddings with Conv1D for sequential reasoning
• Processed 200K+ scientific sentences, dramatically reducing manual literature triage time
• Enables researchers to rapidly identify relevant studies by abstract section

Tech Stack: Computer Vision • MobileNet • Transfer Learning • TensorFlow

Deep learning classifier for fine-grained dog breed recognition.

• Trained CNN model achieving 76% validation accuracy across 120 dog breeds
• Evaluated multiple architectures, selecting MobileNet for optimal performance-to-efficiency ratio
• Built complete ML pipeline: data preprocessing → augmentation → training → inference
• Implemented real-time prediction interface for user uploads

Tech Stack: EfficientNetB0 • Transfer Learning • TensorFlow • Keras

Fine-grained classification system for the CARS196 dataset.

• Fine-tuned EfficientNetB0 on 16,000+ images across 196 car model classes
• Performance: 99% training accuracy | 78% validation accuracy | 75% test accuracy
• Implemented advanced data augmentation strategies to combat overfitting
• Deployed on Google Colab for accessible experimentation

Tech Stack: CNN • TensorFlow • Keras

Binary image classification achieving exceptional performance.

• Built custom Convolutional Neural Network from scratch
• Achieved 98%+ accuracy on 11,000+ test images from Kaggle
• Trained on FreeCodeCamp's Cat & Dog Challenge Dataset
• Demonstrates mastery of fundamental computer vision techniques

Tech Stack: Pandas • Scikit-learn • XGBoost

Binary classification model for identifying potential mental health concerns from survey data.

• Performed comprehensive EDA and feature engineering on sensitive healthcare data
• Implemented multiple ML algorithms with hyperparameter tuning
• Delivered actionable insights for early intervention strategies

Tech Stack: Random Forest • Scikit-learn • NumPy

Automated loan approval decision system for financial institutions.

• Developed Random Forest Classifier achieving 90% accuracy
• Engineered features from applicant financial and demographic data
• Created interpretable model supporting fair lending practices

Tech Stack: LightGBM • Pandas • Seaborn

Regression model predicting user engagement metrics.

• Built LightGBM regressor on custom podcast listening dataset
• Performed feature engineering from temporal and behavioral data
• Delivered insights for content optimization and user retention

Industrial Trainee – Machine Learning / AI
University College Hospital (UCH), Ibadan | April 2025 – October 2025

• Completed 6-month industrial training applying AI solutions to medical workflows
• Developed and evaluated ML models for healthcare data analysis with strict privacy standards
• Collaborated with multidisciplinary teams to deploy intelligent systems supporting hospital operations

• 🎓 Machine Learning with Python – freeCodeCamp (2024)
• 🎓 Deep Learning with PyTorch – Udemy (2024)
• 🎓 Google Data Analytics Professional Certificate – Coursera (2024)

✅ Deployed 3+ production-ready ML applications with live web demonstrations
✅ Delivered 76–98% accuracy across Computer Vision and NLP tasks through optimized pipelines
✅ Active Kaggle contributor with strong competition performance
✅ Built comprehensive AI-focused portfolio showcasing end-to-end project lifecycle

• 📧 Email: amusanolanrewaju420@gmail.com
• 💼 LinkedIn: linkedin.com/in/olanrewaju-amusan
• 🌐 Portfolio: st-lexy.github.io
• 📱 Phone: +234 704 577 3561

💡 Open to collaboration on AI/ML projects, research opportunities, and challenging problem-solving initiatives.

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