Chavi V.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

AI/ML for NLP text classification

NLP / supervised machine learning

Top Data Types

Text

Image

Document

Top Task Types

Fine-tuning

Text Generation

Object Detection

Computer Programming/Coding

Freelancer Overview

AI & Machine Learning Intern, Edunet Foundation & AICTE (Remote) (Jun 2025 – Jul 2025). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Bachelor of Technology, Inderprastha Engineering College (2027) and Oracle Certified Generative AI Professional and Foundations Associate, Oracle University (2025). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

AI & Machine Learning Intern (Jun 2025 – Jul 2025)

OtherTextFine-tuning

Built end-to-end NLP pipelines for text classification from scratch, including tokenization, feature extraction, and model evaluation. Designed and trained multiple supervised ML models to improve classification accuracy on real-world data. Implemented model development steps consistent with AI training and dataset-driven evaluation. • Developed NLP preprocessing and feature extraction workflows • Trained 3+ supervised ML models using scikit-learn and pandas • Implemented tokenization and evaluation from scratch • Achieved up to 85% classification accuracy

2025 - 2025

AI & Machine Learning Intern, Edunet Foundation & AICTE (Remote) (Jun 2025 – Jul 2025)

Don't discloseText

Conducted supervised ML model development work using real-world datasets to achieve classification performance. Focused on preparing inputs and running model evaluation workflows as part of NLP and ML pipelines. The role emphasized iterative training and assessment rather than UI/software engineering duties. • Trained supervised classification models using Python tooling • Performed NLP text classification pipeline steps (tokenization, feature extraction) • Evaluated model performance against real-world dataset outcomes • Delivered up to ~85% classification accuracy results

2025 - 2025

Education

Work History