Consider a machine learning model that classifies images. If your dataset is composed of 100×100-pixel images, then your problem space has 10,000 features, one per pixel.

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even ...

This catch is not specific to linear regression. It applies to any machine learning model in any domain — if the features available aren’t related to the phenomenon you’re trying to model ...

Researchers at Pennsylvania State University examined whether machine learning could predict the risk and contributing ...

A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, offering a more reliable and efficient alternative to traditional estimation ...

Of these, the supervised model is a better choice for developing soft sensors or creating predictive tags. Although there are hundreds of supervised machine learning models, only a handful of ...

In this online data science specialization, you will apply machine learning algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Beginning ...

Each machine learning model has a set of “learned parameters,” whose number and relations vary depending on the type of algorithm and architecture used. For instance, simple regression ...

A machine learning model bests traditional methods for predicting cirrhosis mortality among hospitalized patients.

Model Builder is a UI tool in Visual Studio that helps devs build, train and consume custom machine learning models in those apps. Heading the highlights of ML.NET 2.0 are new APIs for working with ...