While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. With data analytics and pattern recognition, there’s a theory that computers can learn without being programmed to perform specific tasks; researchers are interested in artificial intelligence and wanted to see if computers could learn from data. The iterative aspect of machine learning is important because when the models are exposed to new data, they can independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. And by building precise models, an organization has a better chance of identifying profitable opportunities or avoiding unknown risks.
Here are some examples of machine learning applications that you may encounter in daily life:
- Self-driving car
- Recommendations on Netflix – they recommend movies to you base on your previous choices
- Fraud detection – Spam emails and delete for you automatically
Most industries working with large amounts of data have recognized the value of machine learning technology. By getting insights from data – often in real time – organizations can work more efficiently or gain an advantage over competitors. Financial services, government, health care, sales & marketing, transportations etc.
“Humans can typically create one or two good models a week; machine learning can create thousands of models a week.” — Thomas H. Davenport, Analytics thought leader (excerpt from The Wall Street Journal)
