machine-learning

This project is designed to analyze text for the presence of sarcasm. It uses machine learning models to classify input text and determine whether it contains sarcasm.

The project utilizes the following technology stack:

FastAPI - for creating the API interface and handling requests Docker - for packaging the application and its dependencies into a container Machine learning models for text sarcasm classification

Pulmo is a web-based application that provides an easy-to-use interface for users to input their medical history and other relevant data to get a prediction of their risk of developing lung cancer. The project has also highlighted the importance of data preprocessing and feature selection in improving the performance of machine learning algorithms. Exploratory data analysis was also useful in identifying the most relevant features for predicting lung cancer.

Classifier to assign label to particle

audio transcription using OpenAI's whisper-large-v2, deployed as a containerized service

Docker build of postgresql changing the dimension limit for the cube extension, raising it to 350

Machine Learning toolbox for Humans

https://github.com/yandex/rep

Dockerized Python microservice with REST API for verbs conjugation in French, Spanish and Portuguese