Mahmoud Nady Darwish Egypt, Bani Suif 62621 Undergraduate Computer Science Engineering Student MahmoudNadyDarwish@Gmail.com +2011 2120 0497 I am an enthusiastic junior CSE student who has a passion to learn more about Machine Learning and is seeking an opportunity to utilize his skills to develop better solutions for real-world problems. Technical Member at Google Developer Student Club E-JUST branch. Online Arabic instructor, I instructed over 528 online Arabic sessions to students as part of online exchange programs. Volunteer in Resala Charity, where we helped our society in different ways Languages Arabic (Native) English (Fluent Spoken and Written) Japanese (Beginner) Extracurricular Activities Education BS. in Computer Science Engineering, Egypt-Japan University of Science and Technology (E-JUST) Al-Azhar High School with a 93% grade, and my university awarded me a scholarship for my academic achievements. (2020 - Present) (2017 - 2020) (2021 - Present) (2022 - Present) (2016 - 2019) House Prices - Advanced Regression Techniques: I used feature selection’s methods to clean the data and preprocess it. Also, I used Linear Regression, Random Forest and XGBoost algorithms to find the best fit-line with accuracy 83%. Titanic - Machine Learning from Disaster: I have implemented a model that predicts which passengers survived the Titanic shipwreck. The score of the model is 84.5%. Projects (November 2022) NASA Space Apps Competition, Nile University, Egypt. [Team Leader] Designed and developed a desktop application to track the launching of the space rockets in the real-time. ACM S3edy contest, Minia University, Egypt. [Partcipant] - Problem Solving Hackathon. Competitions (2018) (2018) Python NumPy Matplotlib SciPy HTML Scikit-Learn Pandas C# and C++ GitHub CSS Skills Artificial Neural Network: To classify data, I created a full Artificial Neural Network from scratch. As a matter of fact, I concentrated on the ANN's basic concept and employed the LReLU and Sigmoid functions as activation functions. The used dataset was breast cancer. Linear and Multi Regression Models: I created basic models from scratch to determine the best fit line for a dataset. The code is adaptable enough to handle single and multivariable input. SVM Spam Emails Classification Model: I used Sklearn to partition the data, train it, test it, and calculate the accuracy, recall, and score, which was 91%. K-Means: I break down the basic idea of K-Means Clustring, with an effective implementaion for important Unsupervised Macine Learning Algorithm. WindowsForms Application: I developed a WinForms app for a store so the store opreatorcan manage his reports and products. I used C#. Course Projects (2018) (These 4 projects were between May and Sep 2022)