Muhammad Dawood Majid

Student

Islamabd

Summary

I am an ambitious AI student with hands-on experience in natural language processing, deep learning, and predictive modeling. I have strong analytical and problem-solving skills and am passionate about advancing AI technologies. I am eager to contribute to innovative projects in a collaborative environment and make a meaningful impact in the field of artificial intelligence.

Work History

Intern

Artificial Intelligence And Machine Learning (AIM)

06.2023 - 08.2023

I conducted thorough analyses of customer reviews using advanced Natural Language Processing (NLP) techniques to uncover valuable insights.
I collaborated closely with a diverse team of professionals to contribute to the development of cutting-edge AI solutions.
Through hands-on experience, I gained proficiency in applying machine learning to solve real-world challenges effectively.

Education

Bachelor of Science - Artificial Intelligence

Shaheed Zulfikar Ali Bhutto Institute of Science A

Islamabad, Pakistan

04.2001 -

Skills

Languages: Python

Frameworks and Tools: Jupyter Notebooks, Visual Studio Code , Flask

Libraries: Pandas, Numpy, Matplotlib, OpenCV, SpaCy

Deep Learning: Neural Network Architectures (Neural Networks, CNNs, RNNs (LSTMs)), TensorFlow and PyTorch Frameworks for model building and training, Image classification, object detection, and segmentation

Machine Learning: Regression, Classification, Clustering, Ensemble Methods

DB language: SQL

Projects

Real-time Object Detection System Handwritten Digit Recognition

Built a real-time object detection system using YOLOv8 (You Only Look Once) to detect and classify objects in video streams.

Image Classification with Convolutional Neural Networks (CNNs):

Built a deep learning model using the ResNet50architecture and transfer learning to classify different species of flowers with an accuracy of 96%.

Spam and Ham Classification:

Developed a Naive Bayes Multinomial classifier to distinguish between spam and ham(non-spam) emails, achieving an accuracy of 95% in categorizing incoming messages for efficient email filtering.

Real-time face detection system:

Implemented a real-time face detection system using OpenCV and haarcascadeclassifiers, with an average detection time of 0.06 seconds per frame.

Skin Disease Detection Using YOLO v8

Developed an AI system for detecting various skin diseases using the YOLO v8 object detection model. The system identifies and classifies different types of skin lesions in images, achieving an accuracy of 85%

Timeline