Machine learning may find things that humans would miss; furthermore, the more data that is fed to the algorithms, the better they get at identifying trends and patterns.

Machine Learning systems, also called models, are trained by humans to use an algorithm to classify and analyze data, make predictions, and take actions of limited complexity.

Azure Machine Learning has both AutoML, which sweeps through features and algorithms, and hyperparameter tuning, which you typically run on the best algorithm chosen by AutoML.

Computer algorithms are a set of instructions used to calculate and solve a problem. The quality of AI deep learning depends not only on the algorithm but also on the data.

I will start out by explaining what machine learning is, along with the different types of machine learning, and then I will jump into explaining common models.

Deep learning vs. machine learning: what's the difference between the two? We provide a simplified explanation of both AI-based technologies.

A while ago, while browsing through the latest AI news, I stumbled upon a company that claimed to use “machine learning and advanced artificial intelligence” to collect and analyze hundreds of ...

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.

In order for machine learning to work, there must be data to train the algorithms—massive amounts of data. The ongoing global surge of AI in science and industry is due in part to the ...