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Schedule Interview NowMy name is Bakkkmeedeniye Gedara Theekshana . and I have over 6 years of experience in the tech industry. I specialize in the following technologies: MATLAB, Python, Tutoring, C++, C, etc.. I hold a degree in . Some of the notable projects I've worked on include: Advanced Python Gaze Tracking & Controlled Media Playback, Spam email detection using machine learning, Handwritten Digit Recognition using OCR and Machine Learning, Coconut Ripeness Classification with Machine Learning, palm fruit ripeness levels accurately using machine learning, etc.. I am based in Hingula, Sri Lanka. I've successfully completed 26 projects while developing at Softaims.
My passion is building solutions that are not only technically sound but also deliver an exceptional user experience (UX). I constantly advocate for user-centered design principles, ensuring that the final product is intuitive, accessible, and solves real user problems effectively. I bridge the gap between technical possibilities and the overall product vision.
Working within the Softaims team, I contribute by bringing a perspective that integrates business goals with technical constraints, resulting in solutions that are both practical and innovative. I have a strong track record of rapidly prototyping and iterating based on feedback to drive optimal solution fit.
I'm committed to contributing to a positive and collaborative team environment, sharing knowledge, and helping colleagues grow their skills, all while pushing the boundaries of what's possible in solution development.
Main technologies
6 years
4 Years
4 Years
4 Years
Potentially possible
Tsps technology
Discover an innovative Python project that integrates advanced gaze tracking using Mediapipe and Kalman filtering with controlled video/audio playback. Featuring interactive screen calibration, real-t
Spam email detection using machine learning involves training algorithms to differentiate between spam and legitimate emails. It starts with collecting and labeling a large dataset. Preprocessing clea
Build and deploy a machine learning model capable of accurately recognizing handwritten digits from images.Dataset: Use the widely-known MNIST dataset, which contains 60,000 training images and 10,000
innovative approach to classify coconut ripeness levels using advanced machine learning techniques. Learn how AI algorithms analyze coconut color, texture, and other visual cues to determine whether
With a simple snap, the app provides instant feedback on whether the palm fruits are unripe, ripe, or overripe, empowering farmers and agribusinesses to optimize harvest timing and quality. Perfect f
in Programming,Hardware ,electroics engineering
2012-01-01-2015-01-01