Machine Learning Crucial Role for Successful Business

Machine learning (ML) algorithms offers computers to specify and distribute rules which were not exactly explicitly by the developer.

There are entirely a lot of articles dedicated to machine learning algorithms. Here is an attempt to create a "helicopter view" description of how these algorithms are provided in various business areas. This list is not an exhaustive list of course.

The first point is that ML algorithms can help people by helping them to search patterns or dependencies, which are not visible by a human.

Numeric forecasting seems to be the most well-known area here. For a long time computers were seriously used for predicting the behavior of financial markets. Most structure were developed before the 1980s, when financial markets got approach to sufficient computational power. Later these technologies explore to other industries. Since computing power is cheap now, it can be used by even small companies for all kinds of predications, such as traffic (people, cars, and users), sales forecasting and more.

Anomaly disclosure algorithms help people scan lots of data and tested which cases should be checked as anomalies. In finance they can analyze fraudulent transactions. In infrastructure auditing they make it possible to identify problems before them impact business. It is used in manufacturing standard control.

The main idea here is that you should not detail each type of anomaly. You give a big list of various known cases (a learning set) to the system and system use it for anomaly identifying.

Object clustering algorithms offers to group big amount of data using vast range of meaningful specification. A man can't operate efficiently with more than few hundreds of object with many parameters. Machine can do clustering more profitable, for example, for customers / leads qualification, product lists segmentation, consumer support cases classification etc.

Suggestion / resources / behavior forecasting algorithms gives us opportunity to be more efficient interacting with consumers or users by helping them exactly what they need, even if they have not thought about it before. Suggestion systems works really bad in most of services now, but this industry will be improved quickly very soon.

Resource for - https://www.coursera.org/learn/machine-learning/lecture/Ujm7v/what-is-machine-learning

The second point is that machine learning algorithms can replace people. System makes analysis of people's actions, build rules basing on this information (i.e. learn from people) and apply this rules acting instead of people.

First of all this is about all kind of good decisions making batter opportunity. There are a lot of operations which want for standard actions in standard conditions. People make some "standard decisions" and escalate cases which are not standard. There are no reasons, why machines can't do that: documents processing, cold calls, and bookkeeping, first line customer support etc.

And again, the main feature here is that ML does not want for explicit rules definition. It "learns" from cases, which are already resolved by people during their work, and it creates the learning procedure cheaper. Such systems will save a lot of money for business owners, but many people will lose their job.

Another fruitful area is all types of data harvesting / web scraping. Google aware a lot. But when you need to get some collecting structured information from the web, you still require to attract a human to do that (and there is a big chance that result will not be really good). Information aggregation, structuring and cross-validation, based on your preferences and requirements, will be automated thanks to ML. Qualitative analysis of information will still be made by people.

Finally, all this approaches can be used in almost any industry. We should take it into account, when predict the future of some markets and of our society in general.