Machine-learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications you hear about. (For more background, check out our first flowchart ...

Each algorithm can usually be represented by a set of straightforward steps, and programmers often use visual flowcharts to show their thought processes for solving each individual step with code.

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.

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 algorithms are ubiquitous, but what is the relationship between our mind and a machine learning algorithm? How can we leverage science to create the change we want to see?

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 ...

In most cases, the same machine learning algorithms can work with both supervised and unsupervised datasets. The main difference is that unsupervised learning algorithms start with raw data, ...

Titled "Machine Learning for Chemistry: Basics and Applications," this comprehensive review aims to bridge the gap between chemists and modern ML algorithms, ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists ...