Code for AAAI poster 'Training up to 50 Class ML Models on 3 $ IoT Hardware via Optimizing One-vs-One Algorithm'

Machine learning and optimization algorithms from scratch + visualization

A Machine Learning project predicting dementia progression from MRI data using SVM, One-vs-Rest and One-vs-One classifiers

Implemented logistic regression from scratch to train on sign language digits dataset and titanic dataset using one-vs-one and one-vs-all algorithms

Numpy from-scratch implementation of ML Algorithms: Kernel Perceptron, kNN, MLP, and more

A system to predict the category of tweets during times of crisis (like - flood, cyclone, earthquake, wildfires, pandemic etc.) Multilabel classification was done. Here, we have used 24 categories(6 of which are actionable categories).

This project demonstrates multiclass classification using Perceptron and Logistic Regression, implemented from scratch without using built-in libraries. It includes techniques like One-Versus-The-Rest and One-Versus-One for Perceptron, and Softmax for Logistic Regression, with a focus on understanding core ML concepts.

This is a One-vs-One (OvO) Support Vector Machine (SVM) classifier in Python. The classifier is capable of classifying data with four different labels using the OvO method. The OvO method involves training multiple binary classifiers to distinguish between pairs of classes.

Machine Learning models for Alzheimer’s Classification