Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly through the developer.
There are quite a lot of articles dedicated to machine learning algorithms. Here’s a shot to generate a “helicopter view” description of methods these algorithms are applied in different business areas. Their list is not a complete list of course.
The very first point is always that ML algorithms can assist people by helping these to find patterns or dependencies, which aren’t visible by a human.
Numeric forecasting appears to be probably the most well-known area here. For a long period computers were actively employed for predicting the behaviour of financial markets. Most models were developed ahead of the 1980s, when financial markets got usage of sufficient computational power. Later these technologies spread with industries. Since computing power is inexpensive now, it can be used by even small companies for many kinds of forecasting, for example traffic (people, cars, users), sales forecasting plus 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 generate it possible to identify problems before they affect business. It’s found in manufacturing qc.
The primary idea is you shouldn’t describe every type of anomaly. You give a huge list of different known cases (a learning set) to the system and system apply it anomaly identifying.
Object clustering algorithms allows to group big quantity of data using great deal of meaningful criteria. A person can’t operate efficiently with more than few a huge selection of object with many different parameters. Machine are able to do clustering more effective, by way of example, for customers / leads qualification, product lists segmentation, customer service cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides chance to be efficient getting together with customers or users by providing them the key they need, even when they haven’t yet considered it before. Recommendation systems works really bad in many of services now, however this sector is going to be improved rapidly soon.
The next point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing with this information (i.e. study people) and apply this rules acting instead of people.
For starters that is about various standard decisions making. There are tons of activities which require for standard actions in standard situations. People have “standard decisions” and escalate cases which aren’t standard. There won’t be any reasons, why machines can’t accomplish that: documents processing, phone calls, bookkeeping, first line support etc.
For more information about consulting please visit web portal: check it out.