CodeType - Frontend Coding Practice
A lightweight practice website built to explore UI/UX and quick iteration ("vibe coding" exploration) with a clean interface.
Portfolio
Building practical AI/ML prototypes with clear demos.
I focus on applied machine learning, computer vision, and learning-based systems. I enjoy turning ideas into working prototypes and documenting what I learn.
Machine LearningComputer VisionReinforcement LearningPython
About
Short, specific, and focused.
I am an engineering student exploring AI/ML and RL through hands-on builds. I prefer learning by shipping: small products, experiments, and technical guides.
What I am good at
- Building end-to-end ML demos (data + model + simple UI)
- Computer vision experiments using OpenCV
- Writing clear technical documentation that others can follow
What I am currently learning
- Reinforcement learning fundamentals and agent behavior
- Better evaluation and reproducibility for ML experiments
- System design basics for deploying ML services
Work
Featured projects
A small set of projects that demonstrate how I build, document, and ship. Each card links to a live demo or repository.
Movie Recommender System
A recommendation demo that suggests similar movies. Built to practice NLP-style feature extraction and user-facing ML delivery.
Lenovo TB-X306X Rooting Guide
A step-by-step technical guide published on GitHub and shared to the community (XDA). Focused on clarity and reproducibility.
Writing
Technical articles
I write to clarify concepts and document progress. This is also where I share broader research summaries.
The NLP landscape (1960s - 2020s)
A high-level timeline of NLP milestones, from early statistical methods to modern transformer-era systems.
Tooling
Skills and tools
I keep the stack lean and practical. I focus on fundamentals, reproducible experiments, and clear demos.
Core
PythonNumPyPandasscikit-learn
Visualization & Analysis
MatplotlibSeaborn
Computer Vision
OpenCV
Web / Deployment (basic)
FlaskStreamlitGitHub Pages
Other
C++ (basics)
Direction
Long-term interests
I am particularly interested in reinforcement learning and agentic systems - how agents learn from interaction, how we evaluate them, and how we build reliable behavior under constraints.
Why RL?
RL is a practical way to study learning-by-doing. It connects perception, decision-making, and feedback loops, which makes it a strong foundation for building intelligent systems beyond static prediction.
What I want to explore next
- Small agents in simulated environments (clear reward design)
- Better evaluation for agent behavior (robustness, safety, generalization)
- Bridging ML prototypes to deployable services
Contact
Let us connect
Best way to reach me is via LinkedIn or email.
Direct links
Email: vedantkawade.official@gmail.com
GitHub, LinkedIn, and X are linked below.
What to include in a message
- What you are looking for (internship / collab / feedback)
- Links or context (role description, project idea)
- A deadline if time-sensitive
If you prefer a form, integrate Formspree or similar (keeps GitHub Pages simple).