Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly from the developer.
You can find a lot of articles specialized in machine learning algorithms. Here’s a shot to make a “helicopter view” description of the way these algorithms are applied in different business areas. This list just isn’t the full report on course.
The very first point is always that ML algorithms will assist people by helping the crooks to find patterns or dependencies, that are not visible by a human.
Numeric forecasting is apparently one of the most well known area here. For a long time computers were actively utilized for predicting the behavior of economic markets. Most models were developed prior to 1980s, when markets got entry 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 sorts of forecasting, for example 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 could identify fraudulent transactions. In infrastructure monitoring they’ve created it easy to identify troubles before they affect business. It is used in manufacturing quality control.
The main idea here is that you simply shouldn’t describe every sort of anomaly. You allow a large report on 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 great deal of meaningful criteria. A guy can’t operate efficiently with more than few countless object with many different parameters. Machine are able to do clustering more efficient, for example, for patrons / leads qualification, product lists segmentation, customer service cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides possibility to be efficient reaching customers or users by providing them exactly what they need, even though they haven’t thought about it before. Recommendation systems works really bad generally in most of services now, but this sector will likely be improved rapidly immediately.
The next 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 people) and apply this rules acting as an alternative to people.
First of all this can be about various standard decisions making. There are tons of activities which require for normal actions in standard situations. People have “standard decisions” and escalate cases that are not standard. There won’t be any reasons, why machines can’t do this: documents processing, calls, bookkeeping, first line support etc.
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