About
Hello! I'm Makenson, a highly motivated and detail-oriented Full-Stack Software Engineer with a passion for crafting robust and user-friendly solutions.
My journey in the tech world began while pursuing an Associate in Arts On the STEM path at Broward College, I then transferred to Florida Atlantic University, where I declared my major and earned a Bachelor of Science in Computer Science.
I've been actively involved in delivering custom websites using Next.js, React, JavaScript, HTML, and CSS. I specialize in creating seamless, engaging web experiences
Awards & Certifications: Awarded Best Accessibility App at PlutoHacks.
Bachelor of Science in Computer Science from Florida Atlantic University.
Certified in Deep Learning Onramp and Machine Learning Onramp by MathWorks.
Projects
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Title: Hand Gesture Tracker
This is a Open Source hand gesture tracker using Python, OpenCV, TensorFlow.
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Title: Thousandmen.co
This is a custom designed webapp an E-Commerce business using Shopify & JavaScript.
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Title: Amoseb.com
This is a custom designed drop shipping webapp for an E-Commerce business using Shopify & JavaScript.
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Title: Mbjllc.store
This is a custom designed drop shipping web app for an E-Commerce business using Shopify & JavaScript.
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Title: Lucien Lawn Services
This is a custom landing page using Next.js.
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Title: Insitee.github.io
This is an award-winning wesbite for a hackathon using JavaScript, HTML, and CSS.
Project
I built this application in Python and it's source code is on GitHub. The Hand Tracker project is a Python-based solution that utilizes TensorFlow, OpenCV, Mediapipe, Matplotlib, and Scikit-Learn to recognize and interpret hand gestures in real-time. By leveraging deep learning with TensorFlow, the system accurately detects and classifies hand movements. OpenCV and Mediapipe provide the necessary tools for hand tracking and pose estimation. The project incorporates a graphical interface in Jupyter Notebook, enabling interactive visualization of the recognized gestures using Matplotlib. Hand Tracker serves as an invaluable tool for learning and communicating in American Sign Language, while also contributing to the fields of computer vision, gesture recognition, and human-computer interaction.
Technologies
Python
Jupyter Notebook
TensorFlow
OpenCV
Matplotlib
Mediapipe
Scikit-Learn
Workout | Sets | Reps |
---|---|---|
Warm Up | 2-5 mins | |
Quad Curls | 4 | 8-10 |
Hamstring Curls | 4 | 8-10 |
Leg Press | 4 | 8-10 |
Squats | 4 | 8-10 |
Deadlifts | 4 | 8-10 |
Calves | 3 | 10-12 |
Planks | 3 | 1:00 min |
Cardio | 10-15 min |
Workout | Sets | Reps |
---|---|---|
Warm Up | 2-5 mins | |
Lat Pulldown | 4 | 5-8 |
Back Row | 4 | 5-8 |
Curls | 4 | 5-8 |
Tricep Press | 4 | 5-8 |
Bench press | 4 | 6-8 |
Dips | 4 | 8-10 |
Planks | 3 | 1:00 min |
Cardio | 10-15 min |
Workout | Sets | Reps |
---|---|---|
Warm Up | 2-5 mins | |
Glute bridges | 4 | 10-12 |
Hip thrusts | 4 | 8-10 |
Walking lunges | 3 | 12-15 |
Single Leg Press | 3 | 12-15 |
Romain Deadlifts | 3 | 12-15 |
Hyper Extensions | 3 | 12-15 |
Planks | 3 | 1:00 min |
Cardio | 10-15 min |