Mohamed Ahmed Salman
About Mohamed Ahmed Salman
Mohamed Ahmed Salman is a Machine Learning Engineer at Si-Ware Systems in Cairo, Egypt, where he has worked since 2021. He specializes in building classification and regression models and has contributed to the development of the NeoSpectra platform.
Work at Si-Ware Systems
Mohamed Ahmed Salman has been employed as a Machine Learning Engineer at Si-Ware Systems since 2021. He works in a hybrid environment in Cairo, Egypt. In his role, he contributes to the development of the NeoSpectra platform, which serves as a universal material analysis solution utilizing single-chip FT-NIR spectrometers. His responsibilities encompass various aspects of machine learning, including model development and deployment.
Education and Expertise
Mohamed Ahmed Salman holds a Bachelor's degree in Electrical Electronics and Communications Engineering from Cairo University, which he completed from 2015 to 2020. He further enhanced his skills by obtaining a Nanodegree in Deep Learning from Udacity in 2020, which took four months to complete. Additionally, he studied at the Information Technology Institute (ITI), where he earned a diploma in Artificial Intelligence over a nine-month period from 2021 to 2022.
Machine Learning Specialization
Mohamed specializes in building classification models using various techniques such as Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Random Forests. He also develops regression models employing methods like Linear Regression, Partial Least Squares (PLS), Support Vector Regression (SVR), and Decision Trees. His expertise extends to the full machine learning workflow, including data ingestion, engineering, modeling, hyperparameter tuning, and deployment.
Technical Skills and Tools
In his daily work, Mohamed utilizes a range of machine learning libraries, including Numpy, Pandas, and Matplotlib. He applies feature engineering and feature reduction techniques to enhance the performance of machine learning projects. His comprehensive skill set allows him to effectively engage in various stages of machine learning processes, ensuring robust model development and implementation.