Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

In supervised machine learning, researchers give the algorithm an input ‘X and an output Y, and ask it to find the functional mapping Y=F(X) between the X and Y.

Because algorithms, machine learning and AI are pretty much baked into our lives at this point. Related links: More insight from Kimberly Adams.

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 algorithms face two main constraints: Memory and processing speed. Let’s talk about memory first, which is usually the most limiting constraint. A modern PC typically has ...

Deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs. The more layers in the neural ...

Like with movies, I don’t have one favorite machine learning (ML) algorithm, but a few favorites, each for its own reason. Here are some of my top few algorithms and models: Most elegant: The ...

Machine learning is hard.Algorithms in a particular use case often either don't work or don't work well enough, leading to some serious debugging. And finding the perfect algorithm–the set of ...

Machine Learning: A field of artificial intelligence, focused on the creation of algorithms, models and systems to perform tasks and generally to improve upon themselves in performing that task ...

Transparency of machine-learning algorithms is a double-edged sword. ... 2018, redefines how organizations are required to handle the collection and use of EU citizens' personal data. ...