Machine learning (ML) algorithms allows computers to define and apply rules that had been not described explicitly by the developer.
There are lots of articles focused on machine learning algorithms. The following is a shot to generate a “helicopter view” description of how these algorithms are applied to different business areas. A list just isn’t a comprehensive set of course.
The initial point is that ML algorithms can help people by helping the crooks to find patterns or dependencies, that are not visible by a human.
Numeric forecasting looks like it’s probably the most recognized area here. For a long time computers were actively employed for predicting the behavior of financial markets. Most models were developed before the 1980s, when stock markets got usage of sufficient computational power. Later these technologies spread with other industries. Since computing power is cheap now, it can be used by even small companies for those kinds of forecasting, such as traffic (people, cars, users), sales forecasting plus much more.
Anomaly detection algorithms help people scan a lot of data and identify which cases must be checked as anomalies. In finance they are able to identify fraudulent transactions. In infrastructure monitoring they generate it simple to identify issues before they affect business. It really is utilized in manufacturing quality control.
The primary idea is that you simply should not describe every type of anomaly. You provide a large report on different known cases (a learning set) somewhere and system put it on for anomaly identifying.
Object clustering algorithms allows to group big amount of data using number of meaningful criteria. A guy can’t operate efficiently with more than few a huge selection of object with lots of parameters. Machine can perform clustering more effective, as an example, for purchasers / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides opportunity to be more efficient reaching customers or users through providing them the key they need, even when they haven’t yet seriously considered it before. Recommendation systems works really bad for most of services now, but this sector will probably be improved rapidly immediately.
The second 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 from people) and apply this rules acting instead of people.
First of all that is about all kinds of standard decisions making. There are plenty of activities which require for standard actions in standard situations. People develop “standard decisions” and escalate cases which are not standard. There aren’t any reasons, why machines can’t make it happen: documents processing, calls, bookkeeping, first line customer support etc.
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