ERP development
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ERP Systems Administrator and developer (Infor Syteline ERP and Odoo ERP)
A talented, analytical, and highly competent IT candidate with a strong interest in developing a career in the IT industry. My involvement in the IT industry have helped me develop confident problem solving, communication, teamwork, manage multiple tasks and IT Skills. Strong python, Machine learning algorithms, Power BI, ERP (Infor Syteline, Odoo), python libraries (such as Flask web application, Django, Docker, Pytorch, TensorFlow, Scikit Learn, Keras, Kubernetes, Pandas, Matplotlib, Dask, NumPy and Cuml), machine learning models such as KNN, Support Vector Machine, Random Forest, Grid Search, PCA, XGBoost, Naïve Bayes, GANs and SQL Skills demonstrated through project works and work experience.
I have completed Msc in AI from Liverpool JMU. Strong ERP development experience using Python, C# and VB programming languages. I’m also a BCS professional member with MBCS membership. I am now looking for a new role that will present fresh challenges and utilize my experience and potential.
2020 - 2021 Msc in Artificial Intelligence (Machine Learning) (2:1) Liverpool John Moores University (Liverpool, United Kingdom)
Key Coursework & Academic Projects:
Machine Learning Fundamentals: Authored a research paper on bird bone classification, developing classification models using XGBoost, Support Vector Machines, Random Forest, and PCA, with performance evaluation via Grid Search and Confusion Matrices.
Deep Learning: Completed a research project on bird species identification using TensorFlow Object Detection API, implementing Faster R-CNN, EfficientDet, and SSD MobileNet models.
Accelerated Machine Learning: Conducted research on Higgs Boson classification using RAPIDS cuML, leveraging GPU-accelerated machine learning techniques.
Advanced Deep Learning: Developed a CNN-based sound classification system to detect anti-social behaviour in residential areas using TensorFlow.
Enterprise Machine Learning: Researched and implemented ML deployment frameworks, deploying trained object detection models using Flask and Django, with HTML/CSS front-end support and Docker-based containerization.
Research Methods: Applied scientific research methodologies to experimental design, analysis, and technical reporting.
Dissertation Project: Designed and implemented an object detection model using Faster R-CNN for Indo-Malayan animal species classification.
Technical Environment: All academic projects were developed and deployed in a Linux-based environment.
