Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly with the developer.

You’ll find a lot of articles specialized in machine learning algorithms. The following is an endeavor to produce a “helicopter view” description of how these algorithms are used in different business areas. This list just isn’t the full listing of course.

The first point is that ML algorithms will help people by helping these to find patterns or dependencies, which are not visible by way of a human.

Numeric forecasting seems to be probably the most popular area here. For years computers were actively useful for predicting the behavior of economic markets. Most models were developed prior to the 1980s, when financial markets got access to sufficient computational power. Later these technologies spread with other industries. Since computing power is reasonable now, quite a few by even businesses for all kinds of forecasting, such as traffic (people, cars, users), sales forecasting plus much more.

Anomaly detection algorithms help people scan lots of data and identify which cases should be checked as anomalies. In finance they are able to identify fraudulent transactions. In infrastructure monitoring they make it very easy to identify problems before they affect business. It can be found in manufacturing quality control.

The main idea here is you shouldn’t describe each kind of anomaly. Allowing a major report on different known cases (a learning set) somewhere and system put it on for anomaly identifying.

Object clustering algorithms allows to group big level of data using massive amount meaningful criteria. A person can’t operate efficiently with more than few hundreds of object with many different parameters. Machine are capable of doing clustering more effective, by way of example, for customers / leads qualification, product lists segmentation, customer service cases classification etc.

Recommendations / preferences / behavior prediction algorithms provides us possiblity to be efficient getting together with customers or users by providing them exactly what they need, even though they haven’t contemplated it before. Recommendation systems works really bad for most of services now, however, this sector will probably be improved rapidly very soon.

The 2nd point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing about this information (i.e. study people) and apply this rules acting as an alternative to people.

First of all this is about all kinds of standard decisions making. There are a lot of activities which require for normal actions in standard situations. People have “standard decisions” and escalate cases who are not standard. There won’t be any reasons, why machines can’t make it happen: documents processing, phone calls, bookkeeping, first line customer care etc.

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