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Advantages of machine learning techniques
As it was already mentioned, machine learning is a technology that contributes to the development and improvement of dozens of features we have gotten used to. Let's consider the ones we meet most often when using our favorite applications day-to-day and finally learn the advantages of machine learning techniques.
Image recognition
This feature is pretty widespread among mobile applications. For example, it is sometimes used for identification purposes or work with photos including filters and editing. Besides, using different types of machine learning algorithms you can define users' sex and age within an app, implement the recognition of an eye's retina or fingerprint etc. A good example of a machine learning application is the recognition of license plates on roads in case of violations.
Voice recognition
There is no need to look far, Apple's Siri and Google Now use a list of machine learning algorithms to recognize the user's commands and react to them.
Optical character recognition
For users, this feature recognizes documents, credit cards, translate foreign words on pictures etc. It is important to take into account that a text has a variety of characteristics such as the font, size and more. That is why every model of algorithms for machine learning should be built having these characteristics in mind.
Advanced customization
I am talking about the creation of personalized content that will be able to take the users' preferences into account.
One of the machine learning examples is Netflix that uses algorithms to make it possible for users to see recommendations based on their previous views. Thus, having viewed The Matrix, the service will assume that you probably would also like Equilibrium etc. The ways of making suggestions differ from service to service. Some of them base their suggestions on actors, some on genre etc.
Using personalization, you do not only gain more loyal users but make them spend even more money on your service as well. It really works and the examples of machine learning applications in most world-known companies prove that fact. However, one thing is that your customization should be useful and really correspond with customers' needs.
Intelligent data analysis
For this purpose not only machine learning is generally used, but machine learning with Big Data. The reason is that Big Data collects the information, while machine learning processes it as well as learns from it. That is needed in order to make further predictions based on certain data.
The more tandem Big Data and machine learning collects and processes users' data, the more you know about what features they use often or do not use at all. This way, you extend your knowledge of the audience and you can adjust your app in accordance to their preferences to make your service even better.
Such a combination is already applied by Amazon and Google in some of their services.
Sensory data analysis
Among all the possible machine learning applications methods, this technology has found its place in medicine as well. It is not a secret to anybody that a modern iOS or Android apps can keep track of person's heartbeat, count steps etc. However, using machine learning applications, your app will be able to monitor users' activity constantly. It means that users should not change the settings in the app depending on what kind of activity. Whether it is jogging or cycling - the app understands that and makes all the necessary changes automatically.
Machine learning capabilities
Machine learning in business
It is easy to see the practicality of machine learning in business world. You will barely find a sphere where this technology could not be applied. Thus, in healthcare, it could be used to achieve a more detailed recognition of tumors at different stages of development.
Another example is smart home technologies. Here, machine learning can be used to monitor of apartments remotely. For example, there are already several machine learning applications in industry of smart homes. These solutions allow the house owner to learn when there is movement in a house with push-up notifications or messages.
Let the figures speak for themselves. One of the machine learning practical examples is one a big U.S. bank that has applied the technology for analyzing credit card transactions. Underneath, you may see what they got as a result.
Result of machine learning introduction (Source: IBM)
Machine learning services
Now, you at least have an idea of what machine learning is and how useful for businesses and the IT industry in general it is. So, it is high time to learn how to implement these magic algorithms. It is worth noting that there are already several ready-made machine learning tools intended to somehow simplify the work for your developers.
Google launched its machine learning service called Awareness API last year. This service allows developers to understand the context in which customers use their smartphones. In total, there are 7 types of context - time, location, places, activity (jogging, cycling etc.), beacons, headphones, and temperature.
In turn, this API is divided into two components - Snapshot API and Fence API. These tools help in regulation and tracking of some already mentioned context parameters. This technology has already been adopted by the creators of such applications as Trulia and SuperPlayer Music.
full article - Importance of Machine Learning Applications in Various
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