Computer Science student, passionate about Machine Learning and AI in general. I love making Data Science and ML-related projects.
There are two fatal errors that keep great projects from coming to life:
1) Not finishing
2) Not starting
― Buddha Gautama
Conceived and developed a prototype for a python app that uses the webcam to detect gestures and trigger specific actions on a Computer. Implemented using PyTorch and Flask.
Approached CT Scans classification using Shallow and Deep Learning methods. Used PyTorch to implement and fine tune CNNs for the task.
Analyzed the National Institude of Diabetes & Digestive & Kidney Diseases' diabetes dataset and applied resampling and classification techniques to construct a robust aggregated model based on shallow learning methods.
Leveraged transfer learning to construct a model that classifies flower images among 102 species with 98% accuracy.
Utilized opencv to detect human faces in images. Used PyTorch to implement a Deep Learning architecture used to find the dog which resembles a human the most. If a dog images is provided, the program outputs the Dogs' Breed.
Implemented Naive Bayes from scratch and Applied it to Tweets' Sentiment Analysis, the program tags tweets as being Positive/Negative.
Trained a CNN using tensorflow for Emotions classification. Used data augmentation and regularization techniques to get improved results.
Predicted Churn using ML techniques and Implemented a Shiny app that allows to follow employees that have a high churn risk. Deployed the app on shinyapps.io
A website for people to ask questions and get detailed, accurate and trusted answers from verified experts. Collaborated with my teammates (CRVOs) to deliver the project in time for the client.
Collaborated in a team of 6 to make an internal WebApp that manages and keeps track of the ESI's PHD Acess Contest.