Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly through the developer.
You’ll find lots of articles devoted to machine learning algorithms. The following is a shot to produce a “helicopter view” description of methods these algorithms are utilized for different business areas. A list just isn’t a complete set of course.
The initial point is that ML algorithms can assist people by helping these to find patterns or dependencies, that are not visible with a human.
Numeric forecasting seems to be probably the most well-known area here. For some time computers were actively used for predicting the behavior of economic markets. Most models were developed prior to 1980s, when real estate markets got entry to sufficient computational power. Later these technologies spread with industries. Since computing power is inexpensive now, technology-not only by even small companies for many kinds of forecasting, for example traffic (people, cars, users), sales forecasting plus more.
Anomaly detection algorithms help people scan lots of data and identify which cases ought to be checked as anomalies. In finance they’re able to identify fraudulent transactions. In infrastructure monitoring they generate it possible to identify issues before they affect business. It can be used in manufacturing quality control.
The key idea is that you ought not describe each kind of anomaly. Allowing a huge listing of different known cases (a learning set) to the system and system utilize it for anomaly identifying.
Object clustering algorithms allows to group big level of data using massive amount meaningful criteria. A man can’t operate efficiently with over few hundreds of object with a lot of parameters. Machine can do clustering more efficient, for instance, for customers / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us opportunity to be more efficient interacting with customers or users by offering them exactly what they need, regardless of whether they haven’t seriously considered it before. Recommendation systems works really bad generally in most of services now, however, this sector will be improved rapidly very soon.
The second point is always that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing on this information (i.e. study people) and apply this rules acting as opposed to people.
To start with this can be about all kinds of standard decisions making. There are many of activities which require for normal actions in standard situations. People have the “standard decisions” and escalate cases which are not standard. There won’t be any reasons, why machines can’t do that: documents processing, calls, bookkeeping, first line support etc.
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