Machine learning (ML) algorithms allows computers to define and apply rules that had been not described explicitly from the developer.
You’ll find a lot of articles focused on machine learning algorithms. The following is a shot to create a “helicopter view” description of the way these algorithms are used in different business areas. Their list is just not a comprehensive report on course.
The initial point is always that ML algorithms will assist people by helping these to find patterns or dependencies, who are not visible by way of a human.
Numeric forecasting is apparently essentially the most recognized area here. For some time computers were actively employed for predicting the behavior of financial markets. Most models were developed ahead of the 1980s, when stock markets got entry to sufficient computational power. Later these technologies spread with other industries. Since computing power is reasonable now, quite a few by even businesses for all those sorts of forecasting, including traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan lots of data and identify which cases should be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring they’ve created it very easy to identify challenges before they affect business. It can be found in manufacturing qc.
The primary idea is that you shouldn’t describe every sort of anomaly. You provide a large listing of different known cases (a learning set) somewhere and system apply it anomaly identifying.
Object clustering algorithms allows to group big level of data using number of meaningful criteria. A person can’t operate efficiently with more than few a huge selection of object with many parameters. Machine can perform clustering better, for instance, for clients / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us opportunity to become more efficient reaching customers or users by giving them exactly what they need, even when they haven’t considered it before. Recommendation systems works really bad generally in most of services now, however this sector is going to be improved rapidly soon.
The other point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing for this information (i.e. study on people) and apply this rules acting instead of people.
First of all that is about all types of standard decisions making. There are plenty of activities which require for normal actions in standard situations. People have the “standard decisions” and escalate cases which are not standard. There isn’t any reasons, why machines can’t do this: documents processing, phone calls, bookkeeping, first line customer support etc.
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