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.
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.
Because of new computing technologies, machine learning today is not like machine learning of the past. 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 that you may be familiar with:
1. The heavily hyped, self-driving Google car? The essence of machine learning.
2. Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.
3. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
4. Fraud detection? One of the more obvious, important uses in our world today.
Why the increased interest in machine learning?
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. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.
One key to producing smart actions in real time is automated model building. Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly changing, growing volumes of data, “… you need fast-moving modeling streams to keep up.” And you can do that with machine learning. He says, “Humans can typically create one or two good models a week; machine learning can create thousands of models a week.”
How is machine learning used today?
Ever wonder how an online retailer provides nearly instantaneous offers for other products that may interest you? Or how lenders can provide near-real-time answers to your loan requests? Many of our day-to-day activities are powered by machine learning algorithms, including:
1. Fraud detection.
2. Web search results.
3. Real-time ads on web pages and mobile devices.
4. Text-based sentiment analysis.
5. Credit scoring and next-best offers.
Feel free to contact E-SPIN for machine learning infrastructure and application security, infrastructure availability and performance monitoring solution.
