Machine learning (ML) algorithms allows computers to define and apply rules that have been not described explicitly by the developer.
There are quite a lot of articles focused on machine learning algorithms. Here’s an endeavor to create a “helicopter view” description of how these algorithms are applied in different business areas. This list is just not an exhaustive listing of course.
The very first point is the fact that ML algorithms can assist people by helping these to find patterns or dependencies, that are not visible by way of a human.
Numeric forecasting seems to be probably the most well known area here. For a long period computers were actively employed for predicting the behavior of monetary markets. Most models were developed ahead of the 1980s, when markets got access to sufficient computational power. Later these technologies spread with industries. Since computing power is inexpensive now, quite a few by even small companies for those sorts of forecasting, for example traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan a lot of data and identify which cases must be checked as anomalies. In finance they could identify fraudulent transactions. In infrastructure monitoring they generate it very easy to identify troubles before they affect business. It really is used in manufacturing quality control.
The principle idea here is that you should not describe every sort of anomaly. Allowing a big listing of different known cases (a learning set) to the system and system put it on for anomaly identifying.
Object clustering algorithms allows to group big level of data using number of meaningful criteria. A guy can’t operate efficiently using more than few numerous object with lots of parameters. Machine can do clustering more efficient, for instance, for purchasers / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides for us opportunity to be a little more efficient interacting with customers or users by giving them the key they need, regardless of whether they haven’t yet considered it before. Recommendation systems works really bad for most of services now, however this sector is going to be improved rapidly quickly.
The second point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing about this information (i.e. learn from people) and apply this rules acting instead of people.
For starters this really is about all sorts of standard decisions making. There are tons of activities which require for normal actions in standard situations. People have the “standard decisions” and escalate cases which are not standard. There isn’t any reasons, why machines can’t accomplish that: documents processing, cold calls, bookkeeping, first line customer service etc.
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