About.
Hi! My name's Ahmed Samady, a software engineer and a junior ML/DL engineer originally from Larache, Morocco. I am currently pursuing a Master's degree in AI and Data Science at the Faculty of Sciences and Technologies of Tangier, where I also earned my Bachelor's degree in Computer Science. I have a strong interest in artificial intelligence, particularly in natural language processing (NLP) and generative AI, as well as machine learning and deep learning. My ability to learn fast allows me to quickly grasp new concepts and technologies in this rapidly evolving field. I excel in critical thinking, which is crucial for solving complex problems in AI and software development. Additionally, my team leading skills enable me to effectively collaborate and guide projects to successful completion. I am highly adaptable, easily adjusting to new challenges and environments in the tech industry. I am proficient in multiple programming languages, including Python, C++, C, and Java, with recent projects primarily involving Python due to its extensive applications in AI and Data Science.
Education.
Master's degree in Artificial Intelligence and Data Science
Faculty of Sciences and Technologies of Tangier / 2023 - 2025 (expected)Currently pursuing a master's degree focused on Artificial Intelligence and Data Science, with an expected graduation in June 2025. This program focuses on various fields including machine learning, deep learning, data mining, natural language processing (NLP), big data, and computer vision. This program provides a comprehensive understanding of advanced AI techniques and data analysis methodologies, equipping me with the skills to develop and implement sophisticated algorithms for diverse applications in today's data-driven world.
Bachelor's degree in Computer Science
Faculty of Sciences and Technologies of Tangier / 2020 - 2023I hold a Bachelor's degree in Computer Science, which provided me with a solid foundation in various fields such as data structures, web development, networking, and operating systems. Additionally, I gained a strong understanding of mathematical disciplines, including linear algebra, calculus, statistics, and probability. This comprehensive education has equipped me with the essential skills and knowledge needed to excel in the diverse and dynamic field of computer science.
Experiences.
PC Halle / Tangier, Morocco
April 2023 - June 2023
InternshipFull-timeOn-siteAs part of my Bachelor's degree in Computer Science, I completed an internship focused on my end-of-studies project. During this internship, I worked on improving the reward system for a mobile children's serious game by implementing different schedules of reinforcement. This project aimed to enhance the game's engagement and educational value, leveraging my skills in software development and understanding of behavioral reinforcement techniques.
Projects.
FSTT Chat Bot
A chatbot for the Faculty of Sciences and Techniques of Tangier (FSTT) using a combination of retrieval-augmented generation (RAG) and fine-tuning techniques. The chatbot is designed to provide accurate and contextually relevant responses to a wide range of queries related to the academic environment at FSTT. The RAG technique is used to extract information from PDF files and generate responses based on the context derived from these embeddings. The fine-tuning process involves adapting pre-trained language models (Llama 3 8B instruct) to understand and generate text specific to the academic context of FSTT. The chatbot is integrated into a user-friendly interface that allows users to choose between the RAG and fine-tuned models based on their preferences or needs. It was developed using a wide range of tools and technologies, including Hugging Face, PyTorch, Kaggle, Google Colab, Unsloth, Langchain/Langserve, SvelteKit, ChromaDB, MongoDB, and Docker. The architecture of the chatbot consists of three Docker containers: the User Interface (UI) container, the API container, and the Model container. The chatbot is deployed using a MongoDB database to store app-specific data, such as conversations and history.
Twitter Sentiment Analysis
Twitter Sentiment Analysis system that leverages a Kafka and Spark pipeline to ingest and analyze Twitter posts in real-time, providing instant sentiment predictions using a pre-trained logistic regression model with cross validation. The user-friendly web interface, developed with Svelte, allows users to initiate and view sentiment analysis jobs. A Flask-based RESTful API facilitates communication between the interface, the processing system, and a MongoDB database that stores prediction results. The entire system is containerized using Docker for seamless deployment and orchestration with Docker Compose, ensuring high portability and manageability.
ML-Toolkit
The project was designed to address the growing need in the fields of Artificial Intelligence and Data Science for effective tools to analyze and interpret the exponentially increasing amounts of available data. Our application provides a comprehensive platform for data preprocessing, machine learning modeling, and data visualization. Developed using the Python programming language and the CustomTkinter library for the user interface, the application is capable of analyzing datasets and implementing various machine learning algorithms, including logistic regression, decision trees, naive Bayes, support vector machines (SVM), Random Forest, and k-nearest neighbors. This general-purpose toolkit aims to bridge the gap in data analysis capabilities, offering an integrated solution for professionals and researchers.
End-of-studies project: GLUPS-123
The goal of this project was to improve a mobile children's serious game by using different reinforcement schedules to improve the game's reward system. We set out to improve the game to make it more entertaining and instructive for kids ages 6 to 8, taking into account the increasing interest in video game development among students and the availability of online self-learning resources. A lot of changes had to be made, like adding player performance statistics and revamping the reward system. We enhanced the user interfaces and gameplay by using Unity and C# programming, which made the learning process more fulfilling and inspiring.