Machine learning (ML) algorithms allows computers to define and apply rules that had been not described explicitly through the developer.
You will find lots of articles focused on machine learning algorithms. Here is an attempt to make a “helicopter view” description of precisely how these algorithms are applied in different business areas. This list isn’t a complete set of course.
The very first point is ML algorithms can assist people by helping them to find patterns or dependencies, which aren’t visible by a human.
Numeric forecasting seems to be the most recognized area here. For a long time computers were actively useful for predicting the behaviour of economic markets. Most models were developed prior to 1980s, when financial markets got use 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 kinds of forecasting, including traffic (people, cars, users), sales forecasting and much more.
Anomaly detection algorithms help people scan lots of data and identify which cases needs to be checked as anomalies. In finance they are able to identify fraudulent transactions. In infrastructure monitoring they generate it easy to identify problems before they affect business. It is used in manufacturing qc.
The principle idea is basically that you shouldn’t describe every type of anomaly. Allowing a huge list of different known cases (a learning set) to the system and system put it on for anomaly identifying.
Object clustering algorithms allows to group big volume of data using great deal of meaningful criteria. A male can’t operate efficiently using more than few hundreds of object with a lot of parameters. Machine can perform clustering more effective, for example, for customers / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides for us possibility to be more efficient getting together with customers or users by providing them the key they need, even though they have not contemplated it before. Recommendation systems works really bad in many of services now, however sector will probably 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 with this information (i.e. study from people) and apply this rules acting as opposed to people.
For starters this is about all kinds of standard decisions making. There are a lot of activities which require for traditional actions in standard situations. People develop “standard decisions” and escalate cases who are not standard. There aren’t any reasons, why machines can’t accomplish that: documents processing, calls, bookkeeping, first line customer service etc.
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