Yenni Delgado Velasco

Key Skills

Software

CVAT

Dataloop

Labelbox

Prodigy

Other

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Action Recognition

Audio Recording

Classification

Computer Programming Coding

Text Generation

Freelancer Overview

I am a dedicated professional with expertise in software development, data analysis, and machine learning, specializing in AI data labeling and model training. With experience in Python development using frameworks like Django and Flask, and proficiency in JavaScript, HTML, CSS, and React, I have worked on end-to-end software projects, including backend and frontend development. Additionally, I have hands-on experience in data analysis with tools like Pandas, NumPy, and Matplotlib, applying these skills to AI training tasks, particularly in natural language processing and image data labeling. I am skilled in working with various data types and labeling techniques, including text, image, and video data, ensuring accuracy and consistency in training AI models. My background in web development, combined with my ability to manage data labeling projects, enables me to contribute effectively to AI and machine learning workflows. I am committed to delivering high-quality labeled data that drives the success of AI systems and am always eager to expand my knowledge and expertise in this field.

Labeling Experience

Multispeaker Audio in Spanish

OtherAudioAudio Recording

This project involved two people having a natural conversation with each other on a wide variety of topics and editing the transcript of that conversation.

This project involved two people having a natural conversation with each other on a wide variety of topics and editing the transcript of that conversation.

2024 - 2024

Text Classification for Sentiment Analysis in Customer Reviews

LabelboxTextClassificationEmotion Recognition

I worked on a text classification project for a retail company to analyze customer sentiment through product reviews. The goal was to label reviews as positive, neutral, or negative, and further identify specific emotions such as happiness, frustration, or satisfaction. This project involved labeling over 5,000 customer reviews, ensuring high accuracy in sentiment categorization and emotion detection. I utilized Labelbox for text annotation and Prodigy for active learning, incorporating ongoing feedback loops to improve the quality of the labeled data. Additionally, I collaborated with a team to refine the labeling guidelines, ensuring consistency across the project. This data was later used to train sentiment analysis models for improving customer service and product offerings.

I worked on a text classification project for a retail company to analyze customer sentiment through product reviews. The goal was to label reviews as positive, neutral, or negative, and further identify specific emotions such as happiness, frustration, or satisfaction. This project involved labeling over 5,000 customer reviews, ensuring high accuracy in sentiment categorization and emotion detection. I utilized Labelbox for text annotation and Prodigy for active learning, incorporating ongoing feedback loops to improve the quality of the labeled data. Additionally, I collaborated with a team to refine the labeling guidelines, ensuring consistency across the project. This data was later used to train sentiment analysis models for improving customer service and product offerings.

2022 - 2022

E-commerce Product Image Classification and Labeling

CVATImageClassificationObject Detection

For a project focused on improving product recommendation systems for an e-commerce platform, I was responsible for annotating product images to categorize them into various product types (e.g., electronics, clothing, home goods). The main tasks included object detection for identifying key items in the images and classifying them into predefined categories. The project involved approximately 10,000 product images, and I ensured high labeling accuracy by following detailed annotation guidelines. Additionally, quality control measures were implemented, including peer reviews and iterative feedback, to maintain the integrity of the data labeling process. This data was later used to train machine learning models to enhance product recommendations and search results.

For a project focused on improving product recommendation systems for an e-commerce platform, I was responsible for annotating product images to categorize them into various product types (e.g., electronics, clothing, home goods). The main tasks included object detection for identifying key items in the images and classifying them into predefined categories. The project involved approximately 10,000 product images, and I ensured high labeling accuracy by following detailed annotation guidelines. Additionally, quality control measures were implemented, including peer reviews and iterative feedback, to maintain the integrity of the data labeling process. This data was later used to train machine learning models to enhance product recommendations and search results.

2022 - 2022

Education

Work History