Machine Learning


Machine Learning is a method of data analysis that automates analytical model building which provides computers with the ability to learn without being explicitly programmed by keeping the focus on the development of computer programs that can change when exposed to new data.
When facing new computer and technologies, Machine Learning of today is nothing like Machine Learning in the past.
Born from pattern recognition and the idea that computers can learn how to perform specific tasks without being programmed to. The interest in artificial intelligence grew, and investigators wanted to see if computers could learn from considering the patterns formed by the data they’ve extracted.
Machine Learning enables them to learn from previous computations and sceneries to produce reliable, repeatable decisions and results. Instead of extracting data in a human comprehension level, Machine Learning uses that data to detect patterns in data and adjust program actions accordingly to it. 
A daily and practically example of Machine Learning processes is Facebook’s News Feed. When a Facebook user stops scrolling to read or “like” a publication of some page, the Machine will learn from that, identifying patterns in the background, adjusting accordingly and as result, start showing more publications of that page.
Machine Learning means that is possible to quickly and automatically produce models that are capable of analyzing faster and more complex data, delivering accurate results on prompt. Its importance is on giving organizations of today a better chance to identify profitable opportunities, build precise models and to avoid unknown risks on a more powerful and affordable way.
Using algorithms to build models that apply complex mathematical calculations on identifying patterns and uncovering connections, allows organizations the capability of making better and faster decisions without the need for constant human intervention and supervision.

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