💻 Software Engineer & AI Specialist
🎓 College of Computers and Artificial Intelligence, Beni Suef University (Graduating 2026)
I’m a passionate software engineer with strong expertise in Artificial Intelligence, Machine Learning, and Computer Vision.
I enjoy building intelligent systems, developing full-stack applications, and creating data-driven solutions for real-world problems.
I’m always eager to learn new technologies and collaborate on innovative projects.
National Telecommunication Institute (NTI) - ETA Training Program (On-Site) - Location (Egypt New Capital)
- Mastered core concepts of AI and Machine Learning, including Supervised and Unsupervised Learning, Feature Engineering, and Model Evaluation, as part of the HCIA-AI V4.0 curriculum.
- Gained advanced practical skills in Deep Learning (CNNs, RNNs, LSTMs, Transfer Learning), Computer Vision (Object Detection, Image Processing), and using frameworks like TensorFlow, PyTorch, and Keras to solve real-world problems.
CodeAlpha – Machine Learning Intern
- Built a Credit Scoring Model to predict creditworthiness using Logistic Regression, Decision Trees, and Random Forest, with metrics such as Precision, Recall, and ROC-AUC.
- Developed an Emotion Recognition System using deep learning (CNN, RNN, LSTM) and MFCC feature extraction on datasets like RAVDESS and TESS.
- Implemented Handwritten Character Recognition with CNNs on MNIST/EMNIST datasets, extendable to word-level recognition.
- Created Disease Prediction Models with SVM, Random Forest, Logistic Regression, and XGBoost on structured medical datasets (diabetes, heart disease, breast cancer).
CodeAlpha – Python Programming Intern
- Designed a Hangman Game with randomized words and limited attempts.
- Built a Stock Portfolio Tracker using dictionaries and file handling to calculate investments.
- Automated repetitive tasks such as moving files, extracting emails, and scraping webpage titles.
- Developed a Rule-Based Chatbot with predefined conversational replies.
Saiket Systems– Data Science Intern
- Conducted data preparation: loading datasets, handling missing values, encoding categorical variables, and splitting data into training/testing sets.
- Performed Exploratory Data Analysis (EDA) to calculate churn rates, visualize customer demographics, and analyze relationships between tenure, contract type, and churn.
- Executed customer segmentation based on tenure, monthly charges, and contract types to identify high-value customers at risk of churn.
- Developed and evaluated churn prediction models using Logistic Regression, Decision Trees, and other algorithms; optimized models through feature selection and hyperparameter tuning.
- Interpreted model outputs with feature importance and ROC-AUC analysis, providing business insights on key churn drivers.
- Delivered business recommendations including targeted retention strategies and engagement tactics to reduce churn and improve customer loyalty.
Codveda Technology – Machine Learning Intern
- Completed a structured internship focused on end-to-end machine learning workflows, including data preprocessing, model building, and evaluation.
- Implemented supervised and unsupervised algorithms such as Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, SVM, and K-Means clustering using Python, scikit- learn, and related libraries.
- Designed and trained a feed-forward neural network using TensorFlow/Keras, achieving hands-on experience in deep learning.
- Applied techniques for data cleaning, feature encoding, normalization, and visualization to prepare datasets for analysis and improve model performance.
- Evaluated models using metrics such as accuracy, precision, recall, F1-score, ROC-AUC, R-squared, and MSE, with practical exposure to cross-validation and hyperparameter tuning.
- Gained experience in presenting results with visualizations (matplotlib, seaborn) and interpreting outcomes to derive actionable insights.
- Introduction to Software Engineering using C++
- AI Diploma
- Overview of AI (Final Exam Score 100 / 100)
- AI Basic (CRA Training Program)
- HCIA-AI V4.0 (Mock Exam score 916 / 1000)
- AI Technology And Applications (Final Exam Score 88 / 100)
- AI For Beginners
- Introduction to Artificial Intelligence
- Introduction to programming
- Python
- Computer Vision
- Data Visualization
- Introduction to Deep Learning
- Introduction to Machine Learning
- Intermediate Machine Learning
- Introduction to SQL (bigquery)
- Introduction to C++
- Introduction to Java
- Introduction to JavaScript
- Intermediate Java
- Introduction to C#
- Intermediate C#
- Introduction to SQL
- Intermediate SQL
- Introduction to Python
- Machine Learning for Beginners
- Introduction to HTML
- Introduction to LLMs
- Prompt Engineering
- JavaFX
- Python Programming Basics
- Artificial Intelligence for everyone
- Artificial Intelligence for juniors
- Practical Machine Learning for data scientists
- Applied Deep Learning
- Deep Learning for Computer Vision
- AI And Machine Learning Foundation
- Mobile Application development by flutter
- Product & Project Management
- Introduction to Generative AI
- Introduction to Large Language Model
- AI for data science
- Introduction to mobile Application development (IBM)
- AI For Oceans