Specialized in Artificial Intelligence during my studies at Monash University, with proficiency in Machine Learning, NLP, and Computer Vision. Skilled in Python, PyTorch, and TensorFlow, showcasing expertise in developing and optimizing supervised and unsupervised learning models. Eager to leverage advanced AI techniques to create meaningful solutions in innovative and dynamic environments.
VibeRight Mar 2024- Jun 2024
· Collaborated in an Agile team to develop a health and nutrition platform featuring the "Healthy Food Detector," an image recognition tool that classifies food as healthy or unhealthy.
· Integrated the project with AWS, strengthening expertise in deep learning and demonstrating the value of teamwork and iterative development.
Image Classification using CIFAR-10 Dataset. Aug 2023- Sep 2023
· Trained a Convolutional Neural Network (CNN) on the CIFAR-10 dataset, achieving 81% accuracyin classifying 10,000 images into 20 categories.
· Enhanced results using data augmentation and knowledge distillation and deployed the solution via AWS SageMaker to ensure scalability and efficiency.
Neural Machine Translator Jan 2022- May 2022
· Developed a Python-based translator for converting English to vernacular languages, leveraging LSTM models, POS tagging, and machine learning techniques.
Fake Review Monitoring System. Jun 2021 - May 2022
· Built a Python-based system using machine learning (ML), deep learning (DL), and NLP algorithmsto detect fake reviews on e-commerce platforms. Enhanced the authenticity of online reviews by effectively identifying fraudulent content, seamlessly integrating advanced technologies to boost trust in the marketplace.
Cancer Detection from Genetic Variations Jan 2021- May 2021
Conducted cancer detection and treatment research in Python employing machine learning libraries to analyze patient genetic data and deployed a variety of ML models, including Naïve Bayes, K- Nearest, Logistic Regression, and Random Forest, to enhance diagnostic precision and treatment strategies.