Hardik Kamboj
Course Mentor at OpenCV University
College Park, Maryland, United States
Summary
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Rockstar🎓
Top SchoolHardik Kamboj is a machine learning and computer vision practitioner with six years of experience spanning research, industry, and mentoring roles. Currently a Course Mentor at OpenCV University, he blends hands-on engineering with deep theoretical curiosity, having pursued foundational topics like linear algebra, calculus, and probability alongside applied projects. His research background includes model compression and knowledge distillation work at IISc, and recent industry experience as an ML engineer at Nyun AI. Hardik contributes practical learning resources on GitHub, including a Python implementation of exercises from "An Introduction to Statistical Learning," demonstrating applied expertise in ensemble methods and SVMs. Beyond tech, he shares insights publicly, mentors learners, and brings a contemplative, Karma Yogi perspective inspired by interest in ancient Indian philosophy and a preference for a slow, nature-centered life.
6 years of coding experience
1 year of employment as a software developer
IK Gujral Punjab Technical University
English, Hindi, Tamil, Punjabi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
scikit10
pandas10
regression10
decision-tree10
machine-learning10
support-vector-machine10
random-forest10
python10
scikit-learn10
k-means-clustering9
Programming languages (4)
Github contributions (5)
hardikkamboj/An-Introduction-to-Statistical-Learning
Jun 2020 - Apr 2021
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
Role in this project:
Data Scientist
Data ScientistContributions:100 commits, 2 PRs, 87 pushes in 9 months
Contributions summary:Hardik is contributing to a repository focused on statistical learning by implementing solutions and providing code for the exercises and examples provided in the textbook "An Introduction to Statistical Learning". The commits demonstrate hands-on application of machine learning methods, including implementation of polynomial regression, support vector machines, decision trees, bagging, random forests, and boosting techniques. The user's work is centered on analyzing and modeling data with a focus on key concepts in statistical learning.
statisticspythondata-sciencestatistical-learningstatistical
hardikkamboj/hardikkamboj
Nov 2020 - Oct 2021
Contributions:15 commits, 14 pushes, 1 branch in 11 months
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