yousaf2018 Goto Github PK

followers: 12.0 following: 8.0 repos: 127.0 gists: 1.0

Name: Mahmood Yousaf

Type: User

Bio: I am keenly interested to work on machine learning, deep learning, and data analysis projects.

Location: Pakistan

Blog: https://www.linkedin.com/in/mahmood-yousaf/

Hi I am Mahmood Yousaf

I am machine learning engineer with a bachelor's degree in computer science from Namal University Mianwali. I am currently working at Neurog, where I have gained hands-on experience in computer vision and machine learning. With a strong background in computer science, I have developed a deep understanding of machine learning algorithms and techniques and skilled in using a variety of tools and technologies to build and deploy machine learning models. In my role at Neurog, I have demonstrated the ability to translate business needs into practical machine learning solutions and find innovative ways to apply machine learning to real-world challenges. I am dedicated to delivering high-quality work on every project.

Tech Stack

Languages

Database Tools

Visualisation Tools

Development Tools

Data Manipulation Tools

Machine Learning Tools

Backend Tools

Web applications development

Cloud Platforms

Android applications development

Desktop applications development

Mahmood Yousaf's Projects

data-analysis-project-using-pandas-numpy-matplotlib

data-structures-and-algorithms-in-python-practice-exercises

data_structure_in_java

deep-learning-models-integraton-with-flask

deepface

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

distance_estimation_using_yoloer_v1

YOLOER stands for You Only Look Once and Estimate Range. The project is about REAL-TIME Object Detection and Distance/Depth Estimation using YOLOv5 as object detector.

docker-practice

doubly-linked-list-using-c-

driver-drowsiness-detection

A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of eyes) and measurement of mouth aspect ratio (to detect yawning).

drowsiness_detection

A simple Drowsiness Detection module for humans. 😴 🚫 🚗

dsa_project

face-recognition-liveness

Face detection and recognition + liveness detection and spoofing attack recognition using onnxruntime. Includes an easy-to-use Flask API and Dockerfile.