Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly with the developer.
You can find lots of articles specialized in machine learning algorithms. Here’s an attempt to generate a “helicopter view” description of the way these algorithms are used in different business areas. Their list is not the full set of course.
The first point is ML algorithms will assist people by helping the crooks to find patterns or dependencies, which aren’t visible with a human.
Numeric forecasting seems to be essentially the most well known area here. For some time computers were actively employed for predicting the behaviour of financial markets. Most models were developed prior to the 1980s, when real estate markets got access to sufficient computational power. Later these technologies spread with other industries. Since computing power is cheap now, technology-not only by even small companies for all those types of forecasting, such as traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan plenty of data and identify which cases must be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring they make it very easy to identify problems before they affect business. It can be found in manufacturing quality control.
The principle idea is that you ought not describe every type of anomaly. You allow a major list of different known cases (a learning set) to the system and system use it for anomaly identifying.
Object clustering algorithms allows to group big quantity of data using wide range of meaningful criteria. A male can’t operate efficiently using more than few hundreds of object with many parameters. Machine can do clustering more effective, as an example, for patrons / leads qualification, product lists segmentation, customer service cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides us possibility to be efficient getting together with customers or users by providing them exactly what they need, even if they haven’t contemplated it before. Recommendation systems works really bad generally in most of services now, but this sector will likely be improved rapidly quickly.
The second point is always that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing on this information (i.e. study people) and apply this rules acting rather than people.
To begin with this is about various standard decisions making. There are many of activities which require for standard actions in standard situations. People make some “standard decisions” and escalate cases who are not standard. There won’t be any reasons, why machines can’t accomplish that: documents processing, calls, bookkeeping, first line customer support etc.
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