Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly with the developer.
You can find a great deal of articles dedicated to machine learning algorithms. Here’s an endeavor to create a “helicopter view” description of the way these algorithms are utilized for different business areas. Their list isn’t a comprehensive report on course.
The first point is that ML algorithms can help people by helping the crooks to find patterns or dependencies, who are not visible by the human.
Numeric forecasting looks like it’s one of the most popular area here. For a long period computers were actively used for predicting the behavior of economic markets. Most models were developed ahead of the 1980s, when financial markets got usage of sufficient computational power. Later these technologies spread to other industries. Since computing power is reasonable now, quite a few by even small companies for many forms of forecasting, such as traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan lots of data and identify which cases should be checked as anomalies. In finance they could identify fraudulent transactions. In infrastructure monitoring they generate it very easy to identify challenges before they affect business. It can be found in manufacturing quality control.
The primary idea is that you shouldn’t describe every type of anomaly. You allow a major set 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 great deal of meaningful criteria. A male can’t operate efficiently with over few numerous object with a lot of parameters. Machine are able to do clustering more effective, for instance, for patrons / leads qualification, product lists segmentation, support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us possiblity to become more efficient a lot more important customers or users by giving them exactly what they need, even when they haven’t thought about it before. Recommendation systems works really bad generally in most of services now, but this sector will be improved rapidly very soon.
The next point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing about this information (i.e. study on people) and apply this rules acting rather than people.
For starters this is about various standard decisions making. There are many of activities which require for normal actions in standard situations. People develop “standard decisions” and escalate cases that are not standard. There won’t be any reasons, why machines can’t do this: documents processing, cold calls, bookkeeping, first line support etc.
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