Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly by the developer.
You will find quite a lot of articles specialized in machine learning algorithms. The following is an effort to generate a “helicopter view” description of how these algorithms are applied to different business areas. This list is not a comprehensive list of course.
The very first point is always that ML algorithms can help people by helping them to find patterns or dependencies, which aren’t visible with a human.
Numeric forecasting seems to be the most well known area here. For years computers were actively utilized for predicting the behaviour of financial markets. Most models were developed before the 1980s, when financial markets got usage of sufficient computational power. Later these technologies spread with other industries. Since computing power is cheap now, you can use it by even businesses for those sorts of forecasting, including traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan a lot 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 simple to identify problems before they affect business. It is found in manufacturing qc.
The primary idea is that you must not describe each kind of anomaly. You give a major report on different known cases (a learning set) to the system and system apply it anomaly identifying.
Object clustering algorithms allows to group big amount of data using wide range of meaningful criteria. A guy can’t operate efficiently with over few a huge selection of object with lots of parameters. Machine are capable of doing clustering more efficient, as an example, for purchasers / leads qualification, product lists segmentation, support cases classification etc.
Recommendations / preferences / behavior prediction algorithms gives us opportunity to be efficient getting together with customers or users through providing them the key they need, even when they have not seriously considered it before. Recommendation systems works really bad for most of services now, but this sector will likely be improved rapidly quickly.
The second point is the fact that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing about this information (i.e. study on people) and apply this rules acting as an alternative to people.
To begin with this is about various standard decisions making. There are many of activities which require for traditional actions in standard situations. People have “standard decisions” and escalate cases which aren’t standard. There won’t be any reasons, why machines can’t do this: documents processing, phone calls, bookkeeping, first line customer care etc.
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