Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

From the first 5 rows of the dataset, we can see that there are several columns available: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, and sex. There also appears ...

Abstract: Recently, linear regression has been a popular image classification technique since it is efficient and easy to implement. However, the current traditional regression methods require to ...

Abstract: Air pollution is a major scenario in the urban areas. The need of analyzing air quality is becoming an important requirement over past years. Atmosphere contains various levels of pollutants ...

Implement Linear Regression in Python from Scratch ! In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...

Department of Chemical Engineering, University of Louisiana, Lafayette, Louisiana 70504, United States Energy Institute of Louisiana, University of Louisiana ...

ABSTRACT: This research aims to develop reliable models using machine learning algorithms to precisely predict Total Dissolved Solids (TDS) in wells of the Permian basin, Winkler County, Texas. The ...

1 Department of Animal Sciences, University of Florida, Gainesville, FL, United States 2 Department of Animal Science, Iowa State University, Ames, IA, United States Background: To address the ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Nowadays, frontiers among different sciences are revealed as diffuse, and as a ...

This article illustrates how to build, in less than 5 minutes, a simple linear regression model with gradient descent. The goal is to predict a dependent variable (y) from an independent variable (X).