Rumeysa İlhan

I began my software development journey in 2024 at 42 İstanbul, where I successfully completed the intensive and disciplined one-month piscine and advanced to the main program. During this time, I worked on several projects using the C programming language, which helped me strengthen my problem-solving and algorithmic thinking skills.

I am currently a first-year Computer Programming student at Istanbul University and plan to graduate in June 2026. Throughout my education, I have been developing skills in various programming languages, including Python, C, HTML/CSS, JavaScript, and C#, and I actively apply them in both academic and personal projects.

I am particularly interested in the field of artificial intelligence and am currently focusing on learning Python to build a strong foundation in this area. I use Git for version control in my projects, regularly write code, and keep up with emerging technologies. My goal is to continuously grow both academically and practically, and to build a strong career in software development.

Born2beroot

Born2beroot is a system administration project developed at Ecole 42. It involves configuring a Debian-based Linux virtual machine with a strong focus on automation and security. Tasks include setting up SSH, configuring the UFW firewall, managing users and groups, defining sudo privileges, enforcing password policies, and automating routine operations using Bash scripting. This project provided a solid understanding of system-level Linux administration and basic cybersecurity practices.

Animal Species Image Classifier

In this project, an animal’s species is predicted based on its image using the Animals-10 dataset from Kaggle. A pre-trained Convolutional Neural Network model, VGG-16, was loaded using Keras in Python and fine-tuned on the dataset. The system classifies images into ten categories: dog, cat, horse, spider, butterfly, chicken, sheep, cow, squirrel, and elephant. This multi-class image classification system was built using TensorFlow, Keras, and Python.

AI-Powered Autocorrect Tool

This project focuses on building an AI-powered autocorrect tool in Python. Initially, the TextBlob library was used to correct spelling errors. However, to overcome its limitations in understanding context, the project was enhanced by integrating more advanced pre-trained NLP models like BERT. The goal was to develop a system capable of correcting grammar and spelling mistakes not just at the word level, but also considering the context of the entire sentence.

Coderspace Yazılım ve Teknoloji Okulu