Through this course, I explored the fundamentals of Generative AI, its relationship to prior AI innovations, and popular generative models. I gained hands-on experience with deep learning using PyTorch and Hugging Face and learned how to fine-tune pre-trained models for specific tasks. The project focused on Parameter-Efficient Fine-Tuning (PEFT) to optimize large models with minimal computational resources. Through this course, I explored the fundamentals of Generative AI, its relationship to prior AI innovations, and popular generative models. I gained hands-on experience with deep learning using PyTorch and Hugging Face and learned how to fine-tune pre-trained models for specific tasks. The project focused on Parameter-Efficient Fine-Tuning (PEFT) to optimize large models with minimal computational resources.

December 2024 – February 2025

This course covers the fundamentals of AI and machine learning, focusing on Azure Machine Learning services. It explores AI tools, Microsoft’s Team Data Science Process, and how to build, train, test, and deploy AI models in the cloud. Key topics include Azure APIs (vision, language, search), model registry, and deployment strategies using Python. This course covers the fundamentals of AI and machine learning, focusing on Azure Machine Learning services. It explores AI tools, Microsoft’s Team Data Science Process, and how to build, train, test, and deploy AI models in the cloud. Key topics include Azure APIs (vision, language, search), model registry, and deployment strategies using Python.

October 2024 – February 2025

 Diploma of Data Science, Epsilon AI Academy.

Completed a comprehensive program in Data Science covering computer science fundamentals, Python programming, SQL, data cleaning, data Preprocessing, and machine learning. Used tools such as Jupyter Notebook, and GitHub to implement the final project Systemic-And-Banking-Crises (github.com).

August 2021 – June 2022

Project (GitHub): Human Resources Dataset Analysis. In Week 1, a relational database was built and cleaned using SQL & Python to organize employee, education, and performance data. Week 2 featured exploratory data analysis to examine retention factors, showing that higher salary and female gender correlate with lower attrition, while overtime and frequent travel raise turnover. Week 3 involved data preprocessing and ML model training for attrition prediction, and Week 4 delivered a Power BI dashboard and final report. 

Contributors include: Mohamed Abd Al-mgyd, Alyaa Ahmed, Fady Romany, Maher, Kareem Wael, and Abdulrahman Zaki.

June 2024 – December 2024

Inferential Statistics,

Mastered inferential statistics concepts including hypothesis testing, confidence intervals, and regression analysis. Utilized statistical tools and methods to draw meaningful conclusions from data and support data-driven decision-making using R.

From Coursera            

   August 2022 – November 2022

Introduction to Probability and Data with R

Acquired foundational skills in probability theory and data analysis using R. Focused on data manipulation, exploratory data analysis, and visualization techniques to interpret and communicate data insights effectively.

   February 2022 – April 2022

Completed a study of advanced data analysis techniques, including data visualization, statistical modeling, and predictive analytics for data-driven decision-making. 

From Udacity (FWD)   

February 2021 – April 2021

Specialized in advanced data analysis methodologies, gaining expertise in complex data sets, and advanced statistical techniques, With Applied knowledge through hands-on projects and real-world data challenges to enhance analytical skills.

From Udacity (FWD) 

    August 2021 – November 2021

Has Successfully Completed the University Program of Training in the Field of Banking Transactions & Stock Exchange Indices from University of Cambridge, United Kingdom by ‏Dr Ahmed Abdellakher.

Grade: Excellent

Issued On: 01/02/2021