IEEE

Regression and Machine Learning Methods for Predicting Human Movements Based on Skeletal Data

Abstract: In recent years, the advancement of computer vision technologies has facilitated the extraction of skeletal data from human movements captured in video sequences. Leveraging this wealth of ...
The CPA Journal

Automating Data Analysis with Python Dashboards

In today's data-rich environment, business are always looking for a way to capitalize on available data for new insights and ...

Cai Cai's Machine Learning Sklearn Class: Understandable from Scratch, from Model Invocation to Practical Implementation

The design of sklearn follows the "Swiss Army Knife" principle, integrating six core modules: Data Preprocessing: Similar to ...
Live Science

Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use ...
Visual Studio Magazine

Linear Regression Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...
Gehost op MSN

Complete | What Is Linear Regression Machine Learning

This video is a one stop shop for understanding What is linear regression in machine learning. Linear regression in machine learning is considered as the basis or foundation in machine learning. This ...
Frontiers

Regression-based machine learning models for nitrate and chloride prediction in surface water in a small agricultural sand plain sub-watershed in southwestern Ontario, Canada

Machine learning (ML) models have proven to be an efficient technique for better understanding and quantification of surface water quality, especially in agricultural watersheds where considerable ...
Scientific Research Publishing

Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models ()

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...
IEEE

Cardiovascular Machine Learning: Interpreting Maximum Heart Rate Values from Patient Biometrics Using Linear Regression and Random Forest Models

Abstract: Leveraging machine learning, this study analyzes and interprets patient biometrics to predict heart rates and identify disease trends. Currently, machine learning is limited in its abilities ...