Start your journey into machine learning with EEG time-series data in this easy-to-follow Python project. Perfect for ...

With AI quickly becoming strategic, look for projects that can give you some quick business wins and help you gain the skills to apply machine learning more broadly.

Simple models are much easier to implement today since they’re more accessible.

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

TensorFlow is a Python-friendly open source library for developing machine learning applications and neural networks. Here's what you need to know about TensorFlow.

The first key to a successful machine learning project is an ability to collect, store and quickly access large volumes of data. More data means more side cases and more nuanced and precise models.

It may be the buzziest tech trend of the moment, but machine learning is no easy matter. Before you jump into writing machine learning algorithms, here are the basics you need to start a project.

The payoff is cumulative. (Flickr/rowanbank) By Heather Clydesdale Steve Jobs described computers as “the equivalent of a bicycle for our minds.” Borrowing the Apple, Inc. founder and former CEO’s ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes.

A logistic regression model for predicting suicide risk performed similarly well compared with more complex machine learning models, according to findings published in npj Digital Medicine.