Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.

Machine Learning continues to transform the ways we live our lives and run our businesses. However, the meaning and implications of what machine learning is in 2017 are not fully understood by ...

Machine learning is a subset of artificial intelligence that allows computers to learn from their own experiences — much like we do when picking up a new skill. When implemented correctly, the ...

Machine learning is the field behind a great many of the artificial intelligence programs that we encounter in daily life right now. It’s a method that AI tools use to acquire new information.

“Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need,” said ...

Machine learning is applicable to many real-world tasks, including image classification, voice recognition, content recommendation, fraud detection, and natural language processing.

Machine learning has two big jobs: pattern recognition and prediction. Effective learning uses past experience to successfully generalize, predict, or project the outcome of a new situation.

The machine learning is more of a subset where it allows that artificial intelligence to learn, either being some way a supervised or unsupervised method. Toby Bordelon: Cool, thanks.

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.