Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly through the developer.
You will find quite a lot of articles focused on machine learning algorithms. Here’s a shot to make a “helicopter view” description of methods these algorithms are used in different business areas. Their list is not an exhaustive set of course.
The initial point is the fact that ML algorithms can help people by helping these to find patterns or dependencies, which are not visible by a human.
Numeric forecasting is apparently one of the most well known area here. For years computers were actively used for predicting the behavior of economic markets. Most models were developed prior to the 1980s, when real estate markets got entry to sufficient computational power. Later these technologies spread along with other industries. Since computing power is inexpensive now, quite a few by even businesses for those sorts of forecasting, including traffic (people, cars, users), sales forecasting plus more.
Anomaly detection algorithms help people scan a great deal of data and identify which cases must be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring they’ve created it simple to identify issues before they affect business. It’s utilized in manufacturing qc.
The principle idea here is basically that you should not describe every type of anomaly. You allow a huge listing of different known cases (a learning set) somewhere and system utilize it for anomaly identifying.
Object clustering algorithms allows to group big level of data using great deal of meaningful criteria. A man can’t operate efficiently exceeding few countless object with lots of parameters. Machine can do clustering more efficient, as an example, for purchasers / leads qualification, product lists segmentation, customer care cases classification etc.
Recommendations / preferences / behavior prediction algorithms gives us possibility to be more efficient getting together with customers or users by offering them exactly what they need, even if they haven’t contemplated it before. Recommendation systems works really bad in many of services now, but this sector will likely be improved rapidly immediately.
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 instead of people.
To start with that is about all types of standard decisions making. There are many of activities which require for standard actions in standard situations. People have the “standard decisions” and escalate cases that are not standard. There won’t be any reasons, why machines can’t do that: documents processing, cold calls, bookkeeping, first line support etc.
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