In this repository, You will find the documentations on a daily basis on Machine Learning
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Updated
Oct 5, 2026 - Jupyter Notebook
In this repository, You will find the documentations on a daily basis on Machine Learning
UPIQAL is a 100% automated Full-Reference Image Quality Assessment (FR-IQA) framework. It synthesizes deep feature statistics, probabilistic uncertainty, and spatial heuristics to replace human MOS and output spatially localized diagnostic heatmaps for visual artifacts.
Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.
Classic machine learning problem on predicting defaulting risk for individual borrowers.
🍾 A comprehensive machine learning project using Random Forest algorithm to predict wine quality based on physicochemical properties. Features EDA, model training, hyperparameter tuning, feature importance analysis, and detailed documentation.
A collection of machine learning resources, including code, documentation. Machine Learning Algorithms: A collection of regression and classification models implemented using popular machine learning libraries. This repository aims to provide a comprehensive set of models for various use cases.
Optimize budget allocation across multiple marketing channels to maximize revenue
GeneWalk identifies relevant gene functions for a biological context using network representation learning
Artificial Intelligence, Computer Network, Machine Learning, Python, DAA, Distributed System, Internet of Things (IoT), Data Science and Analysis.
Code playground for commonly used machine learning models and algorithms
The program is written in R which analysis patient's health condition using sentiment analysis and classifies as exist, deteriorate and recover using machine learning algorithm - Naive bayes
A project to survey the possibilities of a graph database Neo4j in building decision tree algorithms using stored procedures.
Python Machine Learning algorithms and experiments. At the moment there is an implementation of k nearest neighbours on features projection algorithm (k-NNFP), and Voting Feature Intervals algorithm.
Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;
A log-based Threat Hunting tool
A java library providing a configurable neural network. Supports supervised learning and genetic algorithm.
A New Classification Method Using Soft Decision-Making Based on an Aggregation Operator of Fuzzy Parameterized Fuzzy Soft Matrices
FL-HDC: Hyperdimensional Computing Design for the Application of Federated Learning (IEEE AICAS2021)
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