iB Diploma Programme: Father Michael McGivney Catholic Highschool
My highschool education
helped me prepare for the STEM field by taking post-secondary level
courses including HL Chemistry, SL Math, and SL Physics, and SL French.
I finished and obtained the iB diploma with 38/42 points and graduated
with honours and early acceptance to engineering at McMaster.
From: 2014-2017
Engineering Physics Co-op, B.Eng: McMaster University
My undergraduate programme
has helped me to learn how to work under pressure by taking some of the most rigorous courses offered by my university.
The Engineering Physics program has allowed me to gain applicable knowledge in the engineering domain
by giving me the freedom to take courses ranging from the Software Engineering field to the Electrical Engineering field
with an additional fundamental knowledge in the underlying Physics used in Engineering Systems.
Some notable past, ongoing, and upcoming courses:
- Engineering Design & Graphics: [A-]
- Engineering Economics: [A]
- Fundamentals of Entrepreneurship: [A+]
- Thermal Systems Design: [A-]
- Electronics I - Analog & Digital Circuits: [A+]
- Electronics II - Circuits with Non-linear Active Components: [A-]
- Electronics III - Embedded Microcontroller Programming: [TBA]
- Signals & Systems for Engineering: [TBA]
- Data Structures, Algorithms, and Discrete Mathematics: [TBA]
- Fundamentals of Machine Learning: [TBA]
From: 2017-2022
Machine Learning & Data Science Bootcamp: Zero to Mastery Academy
This was an extensive bootcamp I took out of interest to learn the basic concepts of Machine Learning Alogrithms and also
the applied theory through projects involving Pandas, Numpy, Matplotlib, Scikit-learn, and Tensorflow 2.
From: 2020
Tensorflow 2 and Keras Deep Learning Bootcamp: Pierian Data
This bootcamp covered
the fundamentals of Neural Networks in Deep Learning applications including ANNs, CNNs, RNNs, NLP, AutoEncoders, and GANs. This involved creating projects using
Pandas, Numpy, Matplotlib, Keras API, and Tensorflow 2.
From: 2019
Pytorch for Deep Learning and Computer Vision: Udemy
This course was the next step for me to further specialize in my interest of applying machine learning to computer vision.
From this course I was able to build neural networks from scratch using Pytorch for applications such as Image Recognition, Transfer Learning, and Style Transfer.
From: 2021