Muhammed Murshid

Key Skills

Software

Appen

Clickworker

Top Subject Matter

llm evaluation in english

Natural Language Processing (NLP): Text classification, summarization, entity recognition.

Computer vision training data for facial recognition, object detection, and image classification.

Top Data Types

Image

Text

Video

Top Task Types

Data Collection

Fine Tuning

Prompt Response Writing SFT

Text Generation

Text Summarization

Freelancer Overview

I have extensive experience in data labeling and managing AI training datasets, which has honed my skills in ensuring high-quality annotations for machine learning models. Over the years, I have worked on diverse projects involving image classification, natural language processing, speech recognition, and object detection. My proficiency with labeling tools such as Labelbox coupled with my attention to detail, has enabled me to produce consistently accurate and reliable datasets that enhance model performance. I am adept at following annotation guidelines, creating labeling workflows, and performing quality checks to ensure data integrity and working on UHRS has provided me with exposure to a variety of data types and labeling requirements, particularly in search engine optimization and natural language processing projects. What sets me apart is my ability to manage large-scale datasets and collaborate with cross-functional teams to refine labeling standards. Additionally, my understanding of data preprocessing techniques, combined with a solid foundation in machine learning concepts, allows me to contribute beyond labeling, ensuring that the data aligns with the specific requirements of the models. My commitment to quality, efficiency, and adaptability makes me a strong candidate for any AI training data role.

Labeling Experience

Data Labeling for Autonomous Vehicle Computer Vision

AppenImageBounding BoxObject Detection

In this project, I worked on labeling a dataset of images for an autonomous vehicle computer vision system. The task involved annotating objects such as cars, pedestrians, traffic lights, and road signs using bounding boxes. The dataset consisted of 10,000 images, and I ensured that each image was labeled with high accuracy. To guarantee quality, I followed a strict quality control process, which included reviewing and verifying the annotations. The project required attention to detail and a strong understanding of computer vision concepts.

In this project, I worked on labeling a dataset of images for an autonomous vehicle computer vision system. The task involved annotating objects such as cars, pedestrians, traffic lights, and road signs using bounding boxes. The dataset consisted of 10,000 images, and I ensured that each image was labeled with high accuracy. To guarantee quality, I followed a strict quality control process, which included reviewing and verifying the annotations. The project required attention to detail and a strong understanding of computer vision concepts.

2024 - 2024

Education

Work History