With machine learning (ML) at the heart of much of modern computing, the interesting question is: How do machines learn? There’s a lot of deep computer science in machine learning, producing models ...

One of the big challenges of developing a machine learning project can be simply getting enough relevant data to train the algorithms. That’s where Superb AI, a member of the Y Combinator Winter 2019 ...

The hype about machine learning (ML) is warranted. Machine learning is not just making things easier for the companies that are taking advantage of it. It’s also changing the way they do business. For ...

Writing an all-encompassing book on Python machine learning is difficult, given how expansive the field is. But reviewing one is not an easy feat either, especially when it’s a highly acclaimed title ...

Marketers are always challenged to raise their machine learning knowledge, but have a few options to do so without being overwhelmed by programming code. Marketers are always challenged to raise their ...

If a cell image is worth a thousand words, drug hunters haven’t been paying attention to most of these. Despite the impressive capabilities of high-content imaging systems to peer into the cell, ...

It turns out there’s a fatal flaw in most companies’ approach to machine learning, the analytical tool of the future: 87% of projects do not get past the experiment phase and so never make it into ...

Many federal agencies are now on the path to understanding how machine learning can apply to their specific needs in predictive analysis for cyber threat detection, automating data breach detection ...

Uber is one of those organizations that rely heavily on data. Each day, millions of trips take place in 700 cities across the world, generating information on traffic, preferred routes, estimated ...