Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly through the developer.
You’ll find lots of articles focused on machine learning algorithms. Here’s a shot to produce a “helicopter view” description of how these algorithms are utilized for different business areas. Their list is not the full listing of course.
The first point is that ML algorithms will assist people by helping these phones find patterns or dependencies, which aren’t visible with a human.
Numeric forecasting seems to be the most popular area here. For years computers were actively useful for predicting the behavior of economic markets. Most models were developed before the 1980s, when financial markets got access to sufficient computational power. Later these technologies spread to other industries. Since computing power is cheap now, you can use it by even businesses for those types of forecasting, like traffic (people, cars, users), sales forecasting and much more.
Anomaly detection algorithms help people scan plenty of data and identify which cases should be checked as anomalies. In finance they’re able to identify fraudulent transactions. In infrastructure monitoring they create it very easy to identify problems before they affect business. It really is used in manufacturing qc.
The primary idea is that you shouldn’t describe each kind of anomaly. You provide a major list of different known cases (a learning set) somewhere and system use it for anomaly identifying.
Object clustering algorithms allows to group big volume of data using number of meaningful criteria. A guy can’t operate efficiently exceeding few countless object with many different parameters. Machine are able to do clustering extremely effective, for instance, for clients / leads qualification, product lists segmentation, customer care cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides chance to be a little more efficient a lot more important customers or users by offering them the key they need, even when they haven’t seriously considered it before. Recommendation systems works really bad in many of services now, however sector is going to be improved rapidly soon.
The other point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing with this information (i.e. learn from people) and apply this rules acting instead of people.
First of all that is about all kinds of standard decisions making. There are plenty of activities which require for traditional actions in standard situations. People develop “standard decisions” and escalate cases that are not standard. There isn’t any reasons, why machines can’t accomplish that: documents processing, cold calls, bookkeeping, first line customer care etc.
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