Nagarathna M

PGP in Artificial Intelligence graduate · Aspiring Machine Learning Engineer

Bengaluru, Karnataka, India

@nagarathna_m

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About

PGP in Artificial Intelligence graduate with a B.Tech in Computer Science and Technology and hands-on experience in Python, machine learning, deep learning, computer vision, and natural language processing. Has built and evaluated predictive and image-based AI applications using common ML/DL libraries and tools.

Education

2026

Post Graduate Program in Artificial Intelligence

Vellore Institute of Technology (VIT), Bengaluru

Artificial Intelligence · 2025 – 2026

Artificial Intelligence · 2025 – 2026
2025

B.Tech in Computer Science and Technology

Presidency University

Computer Science · 2021 – 2025

Computer Science · 2021 – 2025
2020

12th Grade

Jindal Girls Pre University College

2018 – 2020

2018 – 2020

Skills

<3 months

Keras Python OpenCV PyTorch Statistics TensorFlow Regression Probability Data Cleaning Classification Neural Networks Data Collection Machine Learning Model Evaluation Image Processing Feature Selection Transfer Learning Data Preprocessing Model Optimization Feature Engineering Supervised Learning Unsupervised Learning Hyperparameter Tuning Natural Language Processing Convolutional Neural Networks

Tools / apps / platforms

<3 months

Docker Mlflow Kubernetes Google Colab Jupyter Notebook Visual Studio Code

Projects

Next Word Prediction using Deep Learning

Python, Deep Learning

Developed a next-word prediction system using tokenization, sequence generation, and a deep learning model to predict likely next words from input text.
Plant Disease Detection and Treatment Recommendation using Deep Learning ↗

Python, MobileNetV2, Transfer Learning, Q-Learning

Built a deep learning system to identify plant diseases from leaf images and integrated a reinforcement learning agent to recommend disease-specific treatments.
Crop Yield Prediction using Machine Learning

Python, Linear Regression, Decision Tree Regressor, Random Forest Regressor

Developed a machine learning system to predict crop yield using agricultural data; cleaned and preprocessed the dataset, trained multiple regression models, and evaluated performance using MAE and R².

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