collin.joseph.0258@gmail.com | collinapjoseph.github.io | linkedin/collinjoseph0258 | github/collinapjoseph
An Artificial Intelligence and Machine Learning Engineer with a diverse skill pool and continuous growth mindset.
Currently completing a Graduate Certificate in Artificial Intelligence at Seneca Polytechnic.
Python notebook using YOLO26n to detect vehicles in Toronto street camera images with a Gradio UI.
Notebook: collinapjoseph/ml-notebook-vehicle-detection-using-yolo26
Skills & Stack: Machine Learning, Python, PyTorch, Gradio
A Python notebook modelling diabetes diagnosis using Random Forests.
Notebook: collinapjoseph/ml-notebook-diabetes-health-lifestyle-indicators
Skills & Stack: Machine Learning, Python, Scikit-Learn
A Python notebook modelling powerlifting athlete performance using Linear Regression and Decistion Tree.
Notebook: collinapjoseph/ml-notebook-openpowerlifting-regression
Skills & Stack: Machine Learning, Python, Scikit-Learn
A blog website that allows visitors to: view, create, edit and delete blog entries.
Demo: collinapjoseph/web-blog
Stack: HTML, CSS, EJS, Bootstrap, JavaScript, jQuery, Node.js, Express.js
Make Tabletop RPG dice roles via text input.
Demo: collinapjoseph.github.io/ttrpg-dice-roller
Stack: HTML, CSS, JavaScript, jQuery
Browser-based implementation of the "Simon" sequential memory game.
Demo: collinapjoseph.github.io/simon-game
Stack: HTML, CSS, JavaScript, jQuery
Browser-based app where user can play drums using keystrokes or mouse.
Demo: collinapjoseph.github.io/drum-kit
Stack: HTML, CSS, JavaScript
Current GPA: 4.0
Coursework: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotics.
Research Topics: Machine Learning, Signal Processing, Statistical Analysis.
Master's Thesis: mcgill.ca/theses/q237hx09z.
Coursework: Machine Learning, Linear Models, Optimization.
Co-op program (24 months of industry internships in total).
Coursework: Algorithm Design & Analysis, Adaptive Algorithms, Digital Signal Processing.
Advanced frameworks and techniques for Prompting Generative AI tools.
Methods for using Generative AI to streamline data analysis.
Strategies for using Generative AI as AI Agents to enhance productivity.
Five-course specialization developed by Stanford University's Andrew Ng.
Detailed explanation of essential Deep Learning architectures, including: Deep Neural Networks, CNNs, RNNs LSTMs.
Frameworks for structuring Machine Learning projects and Python-based assignments using Tensorflow and Keras.