As the days get passed, we are seeing a massive increase in popularity for conferences, venture investments and queries related to business for machine learning. This seems to happen from a long time, for now, but most technology executives regularly have issues in identifying where their business will actually apply machine learning to business problems.

Best of all, we will reference real business usage cases, along with quotes and viewpoints on how to assemble resolve business issues with ML, from our network of AI researchers and executives. From the conclusion of this article, you will have a very good idea as to if any of your present business challenges could be managed nicely with ML.

When it is possible to structure a set of principles or if-then scenarios, to solve the problem entirely, then there might be no demand for ML in any way. Furthermore, if there’s no precedent for a successful result applying machine learning to a specific issue to which you are developing, it might not be the best foray to the ML world.

For illustrative purposes, it’ll be helpful to record a number of a well recognized commercial use case for machine learning so you can churn up your own application ideas:

Face detection: It’s incredibly difficult to write a set of rules, to allow machines to discover faces, but an algorithm could be trained to identify individuals, for example, those used in Facebook. Many tools for facial detection and recognition are an available source.

Email Spam Filters: Some junk filtering may be done by principles, but a lot of the filtering is contextual based on the inbox content applicable for each specific user. A great deal of e-mail volume and a lot of user’s marking spam makes for a good supervised learning issue.

Product/music/film recommendation: Every person’s tastes are distinct, and tastes change over time. Companies such as Amazon, Netflix and Spotify use evaluations and engagement from a big volume of items to predict what any given user may want to purchase, see, or listen to next.

Speech recognition: There isn’t any single combination of sounds at namely signal human language, and individual pronunciations differ broadly – machine learning may identify patterns of language and help to convert speech to text. Nuance Communications is among the best-known language recognition companies today.

Real-time bidding – Facebook and Google could never write certain rules, to determine which ads a given kind of user is the more than likely to click on. Machine learnings can help identify patterns in the user behaviour and determine which individual ads are more than likely to be applicable to which user.

Credit card transaction’s fraud detection – Like e-mail spam filters, only a tiny part of fraud detection could be done utilizing concrete rules. New fraud methods are continuously being utilized, and systems must adapt to discover these models in real time, coaxing from the common signs associated with fraud.

Is the information clean and new?

Clean data is better than large data, is a similar phrase among experienced data science professionals. In case you have reams of business records for years past, it can have no significance today, particularly in areas where the fundamental business processes change radically year annually, such as mobile eCommerce). In case you have reams of info and disjointed information, then you may have too much cleaning, do it before you can get around to learning from the information collected.

Does your information have existing labels to aid a machine make sense of it?

While unsupervised learning permits for a broad level of applications in making sense of information without labels, it is generally not advised for businesses to leap into, ML with the first application in uncontrolled learning. The low hanging fruits for an ML usage case is likely to spawn from it’s historic, labelled data.

Here are some examples which may help the reader to come up with new ideas:

  1. Facebook has millions of tagged human faces on its own stage, faces which have been already associated with a single person. This gave Facebook the capability to train calculations on a huge volume of labelled data, with millions of faces in all sorts of light conditions and from different angles, allowing the algorithms to be exceptionally refined and conducive to identifying particular human faces.
  2. Google serves billions on search results page and may gauge the viability and the relevance of search results page based on click-through rate of its top lists, page -load time, time-on-page from a particular visitor, and several other factors. It’d be impossible to find a set of hard and fast guidelines for showing the correct search results page, so Google has algorithms which understand what are the best options present, it will be determined by real-time engagement from billions of daily searches.


Machine learning isn’t an easy installation, it is also not one which any future minded business can leave off the table for too long. The machine learning, is a buzzword – it is also because many of them possess a strong and robust business case.