Nowadays, the words "machine learning" are heard much more often in the data processing world. It can be defined as the practice of using algorithms to extract data, learn from it, and then eventually make decisions with minimal human intervention. A standard
machine learning software is comprised of statistical as well as predictive analysis that is used to spot patterns and catch hidden insights based on perceived data.
While artificial intelligence
(AI) is the broad science of mimicking human skills, machine learning is a specific subset of AI that trains a machine how to learn.
Why is machine learning and data important?
Data is the core of any machine learning and artificial intelligence algorithms. It must be supplied in the form that the algorithm understands. Things such as increasing volumes and varieties of available data, computational processing and more powerful, and affordable data storage.
All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And don't all companies want bigger and more accurate data? Additionally, by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Read more on data: Using Big Data in a Lead Generation Search Engine