I'm a recent software engineering graduate. I build with Python and am developing myself toward machine learning and artificial intelligence.
I started university with Java and C#, then transitioned to machine learning for my capstone project. I now work in a Python-based stack — classical ML, data analysis, and web development. When I write code, I break the project into parts, lay the foundations first, and then move to the details; that way, when something breaks, I know where to look.
Case Study
Energy Efficiency Prediction
Selected Works
Image Watermarking Desktop App
A desktop application built with Tkinter that lets users add custom watermarks to their images. Image processing with Pillow, file management, and a clean GUI flow.
Tkinter · Pillow
Text to Morse Code Converter
A Python script for two-way conversion between text and Morse code. Practice with character mapping, input validation, and readable output formatting.
Typing Speed Test
A GUI application that measures typing speed. Focused on real-time calculation, user interaction, and event handling.
Tkinter
Technical Stack
Background
Origins. I wrote my first line of code at 13 — designing a clan banner in HTML for a browser game called Ikariam. I searched on Google, rearranged font functions, hit errors, and worked through to solutions until the design in my head appeared on screen. Years later I realized that loop — researching, reading documentation, trial and error — was a complete software development process from start to finish. By high school I had decided I would become a programmer. My English wasn't strong enough yet and Turkish online resources were scarce, so I started with Java and C books. My interest in robotics and machines drew me to C; today my interest in AI comes from the same place — I see them as functionally similar fields.
How I work. There's no single right way to solve a project. My own preference is to simplify: break it into parts, lay the foundations, then move to the details. Components don't get tangled, and when an error appears I know where to look for it. This approach is the natural continuation of a habit I built within a rote-based education system — choosing to understand the logic of a subject rather than memorize question types. If I grasp the underlying structure, I can solve problems I'm seeing for the first time more easily.
Engineering vision. After graduating I watched the industry shift quickly; I noticed entry-level positions narrowing due to both AI and market saturation. I see this not as a threat but as a reality worth thinking carefully about. For me, AI isn't a source of creativity — it's a partner. The "what" is mine to decide; for the "how" of that decision, I use AI as a collaborator. To keep up with the changing world I'm integrating Claude into my workflow. At the same time, I keep writing code the traditional way, so I don't lose the fundamentals and my growth doesn't stall.
Persistence. I'm not afraid of failure. No matter how many errors I run into, I try again; I don't lose hope. Small steps still count as progress. I value imagination over intelligence: imagination answers the "what", intelligence answers the "how". Without a goal, where does ability alone take you?
Certifications
100 Days of Code: The Complete Python Pro Bootcamp
Dr. Angela Yu · Udemy · 56.5 hours
Software Development on SAP HANA
SkillUp EdTech · Coursera
SAP ABAP Temel Seviye Yazılım Eğitimi
Burak Kocaaslan · Udemy
Microsoft Certified Software Developer
C# · ASP.NET MVC · HTML5/JS/CSS3 · Bilge Adam
Get in Touch
Open to new opportunities and collaborations.