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

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

A Python library for Secure and Explainable Machine Learning

Documentation available @ https://secml.gitlab.io

Follow us on Twitter @ https://twitter.com/secml_py

This project aims to develop a robust and interpretable machine learning model for early detection of cyberattacks within network systems. By analyzing network traffic data and identifying patterns associated with malicious activities, the model strives to improve network security and prevent potential cyber threats.

Vision Transformer applied to skin cancer classification

Project Repository for the MLDS Course

Syetem to predict taxi fare in bhutan

[Uc3m] The Classic ghost-eating game

✭ MAGNETRON ™ ✭: This is a Google Colab/Jupyter Notebook for developing a FACE RECOGNITION PROXIA (B) when working with ARTIFICIAL INTELLIGENCE 2.0 ™ (ARTIFICIAL INTELLIGENCE 2.0™ is part of MAGNETRON ™ TECHNOLOGY)

A multi-label classification problem based on the UCI dataset of the same name.

A multi-label audio classification problem.

A machine learning-based anti-cheat system

Machine Learning toolbox for Humans

https://github.com/yandex/rep

Development of a monitoring system that can identify the quality of the printing on the product using the historical vibration data and the quality and production log data.

An Active Learning framework for Drug Discovery with the ability to create targeted models for particular experiments

UMSI-Wikipedia Text Difficulty Prediction (NLP Project)

Capstone 1: Caterpillar Tube Pricing Prediction & Categorization. Capstone 2:Pipeline Multi-Leak Classification.

In MT-BERT we reproduce a neural language understanding model which implements a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple NLU tasks.

Python course for programmers with a focus on features needed for machine learning