Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly through the developer.
You can find quite a lot of articles devoted to machine learning algorithms. Here’s an endeavor to produce a “helicopter view” description of methods these algorithms are utilized for different business areas. This list is not an exhaustive list of course.
The initial point is always that ML algorithms will assist people by helping them to find patterns or dependencies, which aren’t visible by the human.
Numeric forecasting seems to be probably the most well known area here. For years computers were actively useful for predicting the behaviour of economic markets. Most models were developed prior to the 1980s, when financial markets got entry to sufficient computational power. Later these technologies spread along with other industries. Since computing power is cheap now, quite a few by even small companies for all those types of forecasting, for example traffic (people, cars, users), sales forecasting plus more.
Anomaly detection algorithms help people scan a great deal 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 simple to identify problems before they affect business. It can be found in manufacturing quality control.
The key idea is you should not describe each type of anomaly. You provide a major set of different known cases (a learning set) somewhere and system utilize it for anomaly identifying.
Object clustering algorithms allows to group big level of data using number of meaningful criteria. A male can’t operate efficiently with more than few a huge selection of object with a lot of parameters. Machine can perform clustering better, for instance, for purchasers / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us opportunity to become more efficient getting together with customers or users by offering them the key they need, even if they have not considered it before. Recommendation systems works really bad for most of services now, but this sector will probably be improved rapidly soon.
The second 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 from people) and apply this rules acting as opposed to people.
First of all this really is about all types of standard decisions making. There are many of activities which require for standard actions in standard situations. People develop “standard decisions” and escalate cases who are not standard. There won’t be any reasons, why machines can’t do that: documents processing, phone calls, bookkeeping, first line customer support etc.
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