Search engine for touristic excursions to any place in the world Machine learning (ML) algorithms allows computers to define and apply rules that have been not described explicitly from the developer.
You can find quite a lot of articles devoted to machine learning algorithms. Here is a shot to make a “helicopter view” description of the way these algorithms are applied in different business areas. A list is just not a complete set of course.
The first point is ML algorithms will assist people by helping them to find patterns or dependencies, which aren’t visible by a human.
Numeric forecasting seems to be one of the most well-known area here. For some time computers were actively useful for predicting the behaviour of monetary markets. Most models were developed ahead of the 1980s, when financial markets got usage of sufficient computational power. Later these technologies spread along with other industries. Since computing power is affordable now, technology-not only by even small companies for those kinds of forecasting, like traffic (people, cars, users), sales forecasting plus much more.
Anomaly detection algorithms help people scan a lot of data and identify which cases needs to be checked as anomalies. In finance they could identify fraudulent transactions. In infrastructure monitoring they make it easy to identify challenges before they affect business. It is found in manufacturing quality control.
The principle idea here is that you simply must not describe each type of anomaly. You give a big set of different known cases (a learning set) to the system and system apply it anomaly identifying.
Object clustering algorithms allows to group big quantity of data using great deal of meaningful criteria. A guy can’t operate efficiently using more than few hundreds of object with many different parameters. Machine are capable of doing clustering more efficient, for instance, for customers / leads qualification, product lists segmentation, support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides possibility to be a little more efficient interacting with customers or users by providing them the key they need, even if they have not seriously considered it before. Recommendation systems works really bad in many of services now, however this sector will probably be improved rapidly immediately.
The next point is machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing on this information (i.e. learn from people) and apply this rules acting as an alternative to people.
First of all this really is about all kinds of standard decisions making. There are many of activities which require for traditional 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, cold calls, bookkeeping, first line support etc.
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