An aspiring Machine Learning Engineer documenting my journey, sharing my practice projects, and continuously studying to build a solid foundation in Artificial Intelligence.
While I do not have a formal diploma in computer science, I am deeply committed to learning. I have been teaching myself the mathematical and programming fundamentals of Machine Learning through online coursework and hands-on coding, seeking to build my skills from the ground up.
My goal is to enroll in the SIT x NVIDIA AI Centre (SNAIC) AI Programme. I hope to show the admissions panel that I have done the best I can to learn on my own, and I am eager to learn from experts and industry mentors in a structured, professional setting.
Currently, I am focusing on building my understanding of Python, linear algebra, statistics, and basic data science pipelines. I am trying to build, test, and document simple models to practice what I learn and prepare for more advanced challenges.
Comprehensive practical training in Python data ecosystems and core ML algorithms.
Rigorous foundation in the mathematics underpinning state-of-the-art machine learning algorithms.
Here are some of the projects I am currently working on to practice my machine learning and Python skills. I plan to share interactive demos here as I learn how to build and deploy them.
A search platform that understands context and meaning instead of raw keywords. Utilizes Google Gemini embedding vectors and custom linear algebra (Cosine Similarity) written in NumPy to rank document relevance. Includes an LLM-powered explanation agent that describes the conceptual correlation between queries and matches in real-time.
If you represent the SNAIC admissions committee, or just want to collaborate on machine learning projects, I would love to hear from you.
Send an EmailOr write to: keisenchangqixin@gmail.com
© 2026 Keisen Chang. Built with Tailwind CSS and deployed on Vercel.