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. We’re all familiar with the idea of robots, physical and virtual designed to make our lives easier. But what happens when computer systems start to not only execute pre-set programs, but actually absorb information and become smarter through machine learning?
A tool for evaluating data
Machine learning is nothing new; it’s been used in technology for many years. The term itself was invented as early as 1959, by researchers at IBM. The
concept was born out of the field of pattern recognition, where machine learning would involve algorithms that could evaluate data and make predictions for the future based on historic information.
Here are a few widely publicized examples of machine learning applications you may be familiar with:
– The heavily hyped, self-driving Google car? The essence of machine learning.
– Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
– Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
– Fraud detection? One of the more obvious, important uses in our world today.
What’s required to create good machine learning systems?
– Data preparation capabilities.
– Algorithms – basic and advanced.
– Automation and iterative processes.
– Scalability.
– Ensemble modeling.
Did you know?
– In machine learning, a target is called a label.
– In statistics, a target is called a dependent variable.
– A variable in statistics is called a feature in machine learning.
– A transformation in statistics is called feature creation in machine learning.