Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly by the developer.
There are a lot of articles specialized in machine learning algorithms. Here is a shot to generate a “helicopter view” description of precisely how these algorithms are applied to different business areas. This list is not a comprehensive set of course.
The very first point is that ML algorithms will assist people by helping these to find patterns or dependencies, which are not visible by a human.
Numeric forecasting is apparently essentially the most well known area here. For years computers were actively used for predicting the behavior of financial markets. Most models were developed before the 1980s, when financial markets got entry to sufficient computational power. Later these technologies spread along with other industries. Since computing power is reasonable now, technology-not only by even small companies for all forms of forecasting, such as 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 are able to identify fraudulent transactions. In infrastructure monitoring they make it possible to identify issues before they affect business. It really is found in manufacturing qc.
The primary idea is you should not describe every sort of anomaly. You give a big list of different known cases (a learning set) to the system and system use it for anomaly identifying.
Object clustering algorithms allows to group big amount of data using number of meaningful criteria. A male can’t operate efficiently with over few countless object with many different parameters. Machine are capable of doing clustering more effective, for example, for clients / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides opportunity to be more efficient a lot more important customers or users through providing them exactly what they need, even when they haven’t contemplated it before. Recommendation systems works really bad generally in most of services now, however sector will likely be improved rapidly very soon.
The other point is machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing for this information (i.e. study people) and apply this rules acting instead of people.
For starters this really is about all sorts of standard decisions making. There are a lot of activities which require for traditional actions in standard situations. People have “standard decisions” and escalate cases which aren’t standard. There won’t be any reasons, why machines can’t make it happen: documents processing, calls, bookkeeping, first line customer care etc.
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