Abdelrahman Mohammed

Actively contributed to projects by adapting to dynamic requirements and ensuring 100% task completion under tight deadlines with attention to detail.

Built an end-to-end machine learning pipeline in Python using scikit-learn and TensorFlow on 70,000+ patient records. Achieved 82% prediction accuracy, surpassing Kaggle public benchmarks by 9.27%.

Developed a C-based patient data management and scheduling system handling 500+ records, with encryption for secure access.

Built an interactive 3-page Power BI dashboard with KPIs, trend analysis, and pricing insights using Power Query ETL and custom DAX measures with dynamic cross-filtering.

Earned a Verify Certificate of Competency for building LLM-based applications using prompt engineering techniques.

Completed a Generative AI Diploma covering ML, deep learning, NLP, and computer vision. Built a text classification model on a medical dataset achieving 97% accuracy.

Completed an AI program covering Python for data analysis, probability and statistics, data preprocessing, Power BI visualization, introductory LLM applications, and model deployment.

Completed Cisco Networking Academy training with topics including VLANs, routing protocols, wireless networks, and network security.

Completed an ETA AI course focused on Python, machine learning, and deep learning applications.

Completed the Machine Learning Specialization with hands-on assignments. Studied supervised and unsupervised machine learning techniques.

Pursuing a B.Sc. in Computer and Communication Engineering at Benha University. Coursework includes Python, C/C++, data structures, MATLAB/Simulink, signal processing, statistics, calculus, and CCNA.