Rashmi M

Rashmi M

Student at Vivekananda College of Engineering and Technology, Puttur

Bengaluru, IndiaArtificial Intelligence and Machine Learning
2roles
35skills
3education
2credentials

About

Final-year Artificial Intelligence and Machine Learning student with experience in deep learning, NLP, web development, and AI-powered applications.

Experience

Intern

VisionAstraa EV Academy

Feb 2026 - Present

Web Development Intern

NativeSoftTech

Jul 2025 – Sep 2025

Designed and deployed a personal portfolio website; built a responsive blog website with interactive UI and mobile-friendly layouts; developed and tested a RESTful API using Flask, Postman, and MySQL.

Education

Internshala Trainings

Advanced Excel with AI Certification Course, Advanced Excel with AI

Aug 2025 - Sep 2025

Vivekananda College of Engineering and Technology

B.E., Artificial Intelligence and Machine Learning

2022 – 2026

CGPA: 8.50; Puttur, Karnataka

Shree Sharada Women Pre-University College

PCMB

2020 – 2022

Percentage: 86.16%; Sullia, Karnataka

Skills

C (Programming Language)Powerbi tableauUI/UXMicrosoft ExcelPython (Programming Language)PythonC++JavaMachine LearningDeep LearningNatural Language ProcessingTensorFlowPyTorchScikit-learnOpenCVModel TrainingData PreprocessingEvaluation MetricsDebuggingLLM IntegrationAdvanced ExcelPower BITableauGitHubJupyter NotebookVS CodeStreamlitFlaskPostmanMySQLOCRpytesseractConvolutional Neural NetworksResNetLSTM

Projects

Image Caption Generator

Developed a deep learning model to generate natural language captions for images using a Kaggle dataset. Used CNN (ResNet) for image feature extraction and LSTM networks for sequence-based caption generation. Achieved approximately 85% caption accuracy after preprocessing, hyperparameter tuning, and evaluation.

MedEase: AI-Powered Medical Report Summarization

Built an AI assistant to extract and simplify medical reports using OCR (pytesseract) and NLP. Implemented PDF/image text recognition, automated summarization, simplified explanation generation, and an NLP-based Q&A system for local patient queries. Achieved 80% summarization accuracy and deployed it using Streamlit.