Milan Ghimire
B.E. in Computer Engineering
I hold a bachelor's degree in Computer Engineering and intend to pursue a master's in Computer Science. My primary research interests lie in artificial intelligence and machine learning, areas I actively explore through independent study and hands-on projects.
Projects
Machine Learning Based Event Participants Presence Prediction System with Facial Attendance
A machine learning system that predicts whether each event registrant will show up, then verifies it with webcam facial attendance. A Next.js and Node web app collects registrations in MongoDB, an XGBoost model served by FastAPI predicts Present or Absent, and an OpenCV face-recognition module marks who actually attended, so predicted and real attendance sit side by side.
Motion-Based Burglar Detection
A real-time burglar detector in Python that uses no neural network at all - just classic frame differencing in OpenCV. It compares two frames a few steps apart, boxes whatever moved, stamps a "Burglar Detected!" alert and saves a throttled snapshot to disk. The whole script is explained line by line.
Experience
Junior Web Developer
Marpa Infotech Pvt. Ltd.
Working on hosting infrastructure and CMS-based deployment systems using WHM and WHMCS. Involved in building and maintaining a web hosting service platform.
Programming Language Instructor
Skill Spark Pvt. Ltd.
Taught Python and web fundamentals including HTML, CSS, JavaScript, and React. Covered data structures, logic building, and problem-solving, focusing on developing analytical thinking and strong programming foundations.
Full Stack Development Intern
Yuwasoft Technologies Pvt. Ltd.
Contributed to full-stack application development with focus on structured data handling, API integration, and backend logic. Worked with data flow between frontend and backend systems.
Learnings
My notes on the things I'm learning — reinforcement learning, the maths behind models, and more. Each note is one more pass of understanding.
Machine Learning
Chapter notes for revision on the core ideas of machine learning: how models learn from data, the main algorithms, and how to tell whether a model is actually any good.
Reinforcement Learning
Chapter notes for revision, starting with Markov Decision Processes, policies, returns, and the discount factor.