Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly with the developer.
There are lots of articles dedicated to machine learning algorithms. The following is a shot to make a “helicopter view” description of how these algorithms are utilized for different business areas. This list is not the full listing of course.
The first point is that ML algorithms can help people by helping these to find patterns or dependencies, which aren’t visible by the human.
Numeric forecasting is apparently essentially the most well-known area here. For a long period computers were actively employed for predicting the behavior of monetary markets. Most models were developed prior to 1980s, when markets got access to sufficient computational power. Later these technologies spread along with other industries. Since computing power is cheap now, technology-not only by even small companies for all those forms of forecasting, such as traffic (people, cars, users), sales forecasting plus more.
Anomaly detection algorithms help people scan a lot of data and identify which cases needs to be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring they make it possible to identify issues before they affect business. It’s employed in manufacturing qc.
The primary idea here is you should not describe each kind of anomaly. You provide a huge list of different known cases (a learning set) to the system and system apply it anomaly identifying.
Object clustering algorithms allows to group big quantity of data using number of meaningful criteria. A person can’t operate efficiently with more than few a huge selection of object with lots of parameters. Machine can do clustering more effective, for instance, for purchasers / leads qualification, product lists segmentation, support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us opportunity to be more efficient getting together with customers or users by providing them exactly what they need, regardless of whether they haven’t yet seriously considered it before. Recommendation systems works really bad for most of services now, but this sector will be improved rapidly quickly.
The 2nd point is machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing about this information (i.e. learn from people) and apply this rules acting instead of people.
First of all this really is about all types of standard decisions making. There are many of activities which require for traditional actions in standard situations. People have the “standard decisions” and escalate cases which aren’t standard. There won’t be any reasons, why machines can’t accomplish that: documents processing, calls, bookkeeping, first line customer service etc.
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