Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly through the developer.
You can find quite a lot of articles devoted to machine learning algorithms. The following is an attempt to create a “helicopter view” description of how these algorithms are utilized for different business areas. A list isn’t a comprehensive set of course.
The first point is always that ML algorithms will assist people by helping these to find patterns or dependencies, who are not visible by the human.
Numeric forecasting is apparently probably the most well-known area here. For some time computers were actively used for predicting the behavior of economic markets. Most models were developed prior to 1980s, when stock markets got usage of sufficient computational power. Later these technologies spread along with other industries. Since computing power is cheap now, quite a few by even businesses for many types of forecasting, including traffic (people, cars, users), sales forecasting and much more.
Anomaly detection algorithms help people scan plenty of data and identify which cases needs to be checked as anomalies. In finance they’re able to identify fraudulent transactions. In infrastructure monitoring they create it possible to identify problems before they affect business. It’s utilized in manufacturing qc.
The key idea here is you shouldn’t describe every sort of anomaly. You provide a big report on different known cases (a learning set) to the system and system put it on for anomaly identifying.
Object clustering algorithms allows to group big volume of data using great deal of meaningful criteria. A male can’t operate efficiently with over few hundreds of object with many different parameters. Machine are capable of doing clustering more efficient, by way of example, for purchasers / leads qualification, product lists segmentation, customer care cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us possibility to be efficient getting together with customers or users through providing them the key they need, even though they haven’t thought about it before. Recommendation systems works really bad generally in most of services now, however sector will probably be improved rapidly quickly.
The 2nd point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing with this information (i.e. study on people) and apply this rules acting instead of people.
To start with this is about various standard decisions making. There are many of activities which require for traditional actions in standard situations. People develop “standard decisions” and escalate cases which aren’t standard. There are no reasons, why machines can’t make it happen: documents processing, calls, bookkeeping, first line customer service etc.
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