Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
Evolution of machine learning
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:
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The heavily hyped, self-driving Google car? The essence of machine learning.
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Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
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Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
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Fraud detection? One of the more obvious, important uses in our world today.
Why is machine learning important?
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper 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 by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
What's required to create good machine learning systems?
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Data preparation capabilities.
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Algorithms – basic and advanced.
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Automation and iterative processes.
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Scalability.
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Ensemble modeling.
Did you know?
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In machine learning, a target is called a label.
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In statistics, a target is called a dependent variable.
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A variable in statistics is called a feature in machine learning.
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A transformation in statistics is called feature creation in machine learning.