Susan Kaluki

Key Skills

Software

Appen

Data Annotation Tech

Labelbox

Clickworker

Top Subject Matter

Car

LLM evaluation in English

Media and Content Creation

Top Data Types

Audio

Image

Text

Top Label Types

Object Detection

Text Summarization

Translation Localization

Freelancer Overview

I have hands-on experience in data labeling and AI training data preparation, specializing in organizing and annotating datasets for machine learning applications. My projects include creating a labeled dataset for fruit classification, where I meticulously gathered images, applied accurate labeling, and ensured high data quality through systematic organization and review processes. This project not only enhanced my skills in data annotation but also deepened my understanding of the significance of clean and well-structured data in training effective AI models. In addition to my practical experience, I possess strong attention to detail and a methodical approach to data management, which are crucial for maintaining data integrity. My background in nursing provides me with a solid foundation for labeling healthcare-related datasets, while my diploma in journalism enhances my communication skills and ability to document processes effectively. These qualifications, combined with my proficiency in using various labeling tools, position me as a capable data labeler ready to contribute to AI training projects across diverse industries.

Labeling Experience

Fruit Classification Dataset

ClickworkerImageData Collection

The Fruit Classification project aimed to create a labeled dataset for training machine learning models to classify various types of fruits based on images. This project involved collecting, labeling, and organizing images to facilitate the development of accurate classification algorithms. Specific Data Labeling Performed Image Collection: A total of 30 images of common fruits were collected, including apples, bananas, oranges, grapes, and watermelons. Labeling Methodology: Each image was labeled with the corresponding fruit name, which served as the classification target for the dataset. The labeling was conducted using a structured approach in a spreadsheet, where each image name was matched with its label, ensuring clear associations between images and their categories. Data Organization: The labeled images were organized into folders based on their respective fruit types, enhancing accessibility and ease of use for future modeling efforts.

The Fruit Classification project aimed to create a labeled dataset for training machine learning models to classify various types of fruits based on images. This project involved collecting, labeling, and organizing images to facilitate the development of accurate classification algorithms. Specific Data Labeling Performed Image Collection: A total of 30 images of common fruits were collected, including apples, bananas, oranges, grapes, and watermelons. Labeling Methodology: Each image was labeled with the corresponding fruit name, which served as the classification target for the dataset. The labeling was conducted using a structured approach in a spreadsheet, where each image name was matched with its label, ensuring clear associations between images and their categories. Data Organization: The labeled images were organized into folders based on their respective fruit types, enhancing accessibility and ease of use for future modeling efforts.

2023 - 2023

Education

Work History