Machine learning (ML) is the use of mathematical data models that help a computer learn without direct instructions. It is considered a form of artificial intelligence (AI). Machine learning uses algorithms to identify patterns in data. Based on these patterns, a data model is created for forecasting. The more data such a model processes and the longer it is used, the more accurate the results become. This is very similar to how a person hones skills in practice.

The adaptive nature of machine learning makes it great for scenarios where data is constantly changing, the properties of queries or tasks are unstable, or it is virtually impossible to write code to solve it.

How machine learning relates to AI

Machine learning is considered a subset of AI. An "intelligent" computer thinks like a human and performs tasks on its own. One way to train a computer to mimic human thinking is to use a neural network. It is a series of algorithms, modeled on how the human brain works.

How machine learning relates to predictive analytics
Machine learning is a type of predictive analytics. But there is one important feature. Machine learning is much easier to implement with real-time updates as it provides more data. Predictive analytics typically work with a static dataset and require regular updates.

How machine learning relates to deep learning
Deep learning is a specialized form of machine learning that uses neural networks (NNs) to provide answers. Deep learning algorithms can independently determine the accuracy. They allow information to be classified in the same way as the human brain. Such algorithms underlie some of the closest AI systems to human thinking.

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