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machine-learning

Finally a smart RSS reader which doesn't suck ass or your data.

Ultralytics Python Project Template

A binary classification model to accurately predict whether a mushroom is edible or poisonous based on a given dataset. The dataset, a cleaned version of the original Mushroom Dataset from UCI, includes features such as cap diameter, cap shape, gill attachment, gill color, and others. The goal is to create a reliable model that can assist in identifying potentially harmful mushrooms.

A simple project to predict the price of a house based on the features like: Transaction date, House Age, Distance to the nearest MRT station, Number of convenience stores, Latitude and Longitude.

Predicts wine quality using a fine-tuned LightGBM model. It includes data preprocessing, model training, and evaluation, with experiment tracking and model management using MLflow.

Explores the effectiveness of various pre-trained Convolutional Neural Networks (CNNs) for classifying brain MRI images as containing a tumor or not.

This is a combination of a segmentation model and a detection model trained on seated Buddha Statue dataset collected from Google Images based on YOLO v8. This can be used to segment seated Buddha Statue objects from images, and then add a mask to the statue head. This is expected to be used as inputs to an inpainting work based on GAN.

Fast Flexible Replay Buffer Library

Data Science / Machine Learning Pipeline component for training and deploying ML models using CI

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.

Classifier to assign label to particle

A Python library for Secure and Explainable Machine Learning

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

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

Source code for the article "How to create a Simple Captcha Resolver". Link to my blog: https://daemn.blogspot.com/

Complete Conjugation of any Verb in French, Spanish, Catalan, Portuguese, Italian and Romanian powered by Machine Learning

A Ghidra plugin that renames variables based on machine learning predictions.

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

Example project for calculating and publishing LLM metric with DVC and CML

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.