Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly by the developer.
There are quite a lot of articles specialized in machine learning algorithms. Here’s a shot 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 first point is that ML algorithms can help people by helping these to find patterns or dependencies, that are not visible with a human.
Numeric forecasting seems to be essentially the most recognized area here. For some time computers were actively utilized for predicting the behavior of economic markets. Most models were developed ahead of the 1980s, when real estate markets got entry to sufficient computational power. Later these technologies spread along with other industries. Since computing power is reasonable now, you can use it by even small companies for those kinds of forecasting, including traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan plenty of data and identify which cases ought to be checked as anomalies. In finance they could identify fraudulent transactions. In infrastructure monitoring they generate it very easy to identify problems before they affect business. It really is used in manufacturing qc.
The key idea here is basically that you should not describe every type of anomaly. You provide a huge set of different known cases (a learning set) to the system and system use it for anomaly identifying.
Object clustering algorithms allows to group big quantity of data using wide range of meaningful criteria. A male can’t operate efficiently with more than few numerous object with many different parameters. Machine are capable of doing clustering more efficient, as an example, for patrons / leads qualification, product lists segmentation, customer service cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides possibility to be more efficient getting together with customers or users by providing them exactly what they need, regardless of whether they have not considered it before. Recommendation systems works really bad for most of services now, however, this sector will probably be improved rapidly immediately.
The next point is always that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing about this information (i.e. study from people) and apply this rules acting as opposed to people.
To start with this is about various standard decisions making. There are tons of activities which require for normal actions in standard situations. People have the “standard decisions” and escalate cases that are not standard. There are no reasons, why machines can’t do that: documents processing, cold calls, bookkeeping, first line customer support etc.
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