Machine Learning Computer Science

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RAVIKUMAR 776*******

**************@*****.***

linkedin.com/in/ravi-kumar-259bb8313

Koppala, Karnataka, 583238

Summary

As a computer science graduate, I apply technical expertise to real-world challenges through hands-on projects and academic achievements. Demonstrated a strong commitment to leveraging coding skills to develop solutions that address complex challenges and drive positive results. Through projects, internships, and coursework, I actively deepened my understanding and contributed to meaningful solutions by engaging in hands-on problem-solving and collaborative initiatives. Skills

Education

Vidyavardhaka College of Engineering, Visvesvaraya Technological University Computer Science and Engineering, CGPA: 7.1

2025

Government Polytechnic, Koppal

Computer Science and Engineering

Board of Technical Examinations 60%

Government High School, Bandi

SSLC

2017

Karnataka Secondary Education Examination Board. 65% Experience

Infosys

Java Intern

Sep/2024

Developed and executed automated test scripts using Java, streamlining testing processes and utilizing automation frameworks and debugging tools to enhance efficiency.

DreamBuzz Solutions

Machine Learning Intern

Engineered and deployed machine learning models and interactive dashboards for mental health trend prediction using Python, Scikit-learn, and Flask, resulting in a 98% increase in prediction accuracy.

Utilized advanced artificial intelligence and natural language processing (NLP) methodologies to analyze mental health datasets and optimize deployment workflows, improving operational efficiency by 96%. Certifications

Google Cloud Career Readiness- Associate Cloud Engineer Path Google Cloud Career Readiness -Data Analyst Learning Path Powered by ResumeGemini

Participated in 24-hours National Level Hackathon

Projects

Latex Editor

The application is a web-based platform designed to compute and share mathematical equations. Link

Detection of Fake Kannada Speech using Deepfake Audio

Developed a Flask-based web application to detect deepfake Kannada audio using MFCC feature extraction and deep learning models (CNN and LSTM).

Aimed to enhance audio authentication for security and forensic applications.

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