Machine learning (ML) algorithms allows computers to define and apply rules that have been not described explicitly by the developer.

You’ll find quite a lot of articles devoted to machine learning algorithms. Here is an attempt to generate a “helicopter view” description of how these algorithms are utilized for different business areas. A list isn’t an exhaustive set of course.

The 1st point is always that ML algorithms will help people by helping these to find patterns or dependencies, which are not visible with a human.

Numeric forecasting looks like it’s one of the most recognized area here. For some time computers were actively employed for predicting the behaviour of financial markets. Most models were developed before the 1980s, when real estate markets got entry to sufficient computational power. Later these technologies spread to other industries. Since computing power is inexpensive now, it can be used by even businesses for those kinds 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 ought to be checked as anomalies. In finance they could identify fraudulent transactions. In infrastructure monitoring they create it easy to identify challenges before they affect business. It is utilized in manufacturing quality control.

The key idea is you shouldn’t describe each kind of anomaly. You give a major listing of different known cases (a learning set) somewhere and system apply it anomaly identifying.

Object clustering algorithms allows to group big quantity of data using number of meaningful criteria. A man can’t operate efficiently using more than few hundreds of object with many different parameters. Machine can perform clustering more effective, as an example, for customers / leads qualification, product lists segmentation, customer service cases classification etc.

Recommendations / preferences / behavior prediction algorithms gives us opportunity to be more efficient interacting with customers or users by providing them exactly what they need, regardless of whether they have not thought about it before. Recommendation systems works really bad in most of services now, however this sector is going to be improved rapidly immediately.

The other point is the fact that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing with this information (i.e. study from people) and apply this rules acting as opposed to people.

For starters that is about all kinds of standard decisions making. There are tons of activities which require for traditional actions in standard situations. People develop “standard decisions” and escalate cases which are not standard. There isn’t any reasons, why machines can’t do that: documents processing, phone calls, bookkeeping, first line customer service etc.

To get more information about machine learning consulting please visit web site: click for more info.