Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly with the developer.
You can find a lot of articles specialized in machine learning algorithms. Here is an endeavor to create a “helicopter view” description of the way these algorithms are applied in different business areas. A list isn’t an exhaustive list of course.
The first point is always that ML algorithms will help people by helping them to find patterns or dependencies, who are not visible by way of a human.
Numeric forecasting seems to be one of the most well known area here. For a long time computers were actively utilized for predicting the behavior of economic markets. Most models were developed before the 1980s, when stock markets got entry to sufficient computational power. Later these technologies spread with industries. Since computing power is affordable now, you can use it by even small companies for many types of forecasting, for example 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 simple to identify challenges before they affect business. It is found in manufacturing qc.
The key idea here is that you simply should not describe every type of anomaly. You provide a large list of different known cases (a learning set) somewhere and system use it for anomaly identifying.
Object clustering algorithms allows to group big level of data using number of meaningful criteria. A guy can’t operate efficiently with more than few hundreds of object with many different parameters. Machine can do clustering better, as an example, for patrons / leads qualification, product lists segmentation, customer care cases classification etc.
Recommendations / preferences / behavior prediction algorithms gives us opportunity to be a little more efficient interacting with customers or users by giving them exactly what they need, even if they haven’t yet contemplated it before. Recommendation systems works really bad generally in most of services now, however this sector will be improved rapidly soon.
The 2nd point is the fact 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 instead of people.
To begin with this can be about all kinds of standard decisions making. There are tons of activities which require for traditional actions in standard situations. People make some “standard decisions” and escalate cases who are not standard. There are no reasons, why machines can’t make it happen: documents processing, cold calls, bookkeeping, first line support etc.
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