Many businesses are now embracing new technologies. And machine learning is one of the most sought after. Machine learning is a technology that has been gaining quite a lot of attention lately. Some individuals have categorized machine learning under Artificial Intelligence or AI. Is that classification really accurate in describing machine learning?
Well, we will look at the machine learning in more detail to fully understand what it is really all about. We will also identify how it could be effectively implemented in various industries. Moreover, machine learning comes with its fair share of advantages and disadvantages, all of which will be clearly described.
What is Machine Learning and How Does It Work?
According to Wikipedia, machine learning is a term that was initially coined in the late 1950’s by computer gaming pioneer Arthur Samuel. It is a specialized area of computer science that studies the construction of an algorithm that can advance itself without any human interaction, basically, a computer that can discover more information without the need for programming.
Machine learning is involved with studying and creating algorithms that collect provided data, learn from it, and ultimately, make predictions with it. It takes the provided data and by using a particular algorithm makes comparisons. Machine learning then uses the various comparisons to make its actions more efficient.
There are several computing tasks that rely on machine learning. Most of these are tasks that require designing and programming very detailed algorithms that are not easily done. Some of these tasks may include:
- OCR (Optical Character Recognition) – according to Abbyy, this is simply the conversion of the images of typed and handwritten into machine-encoded text,
- MLR (Machine-Learned Ranking) – Quora tells that MLR deals with using machine learning to construct ranking models used for systems that retrieve data.
Machine learning can also be associated with computational statistics. This field is mainly concerned with making accurate predictions with the use of computers.
It also has several links to a field known as mathematical optimization. Mathematical optimization is specifically responsible for providing machine learning with important features, such as theory and method.
Machine Learning Advantages and Disadvantages
There are so many applications that have features developed with machine learning technology. We have become so accustomed to this technology that it is hard to imagine our apps without it. The following are some of the best features of apps developed with machine learning technologies:
- Voice recognition
Many users of mobile devices like iPhone are familiar with either Apple’s Siri or Google now. Voice recognition technology uses machine learning to enable the software to adapt to the commander’s voice and provide a response when interacted with.
- Image recognition
There is a wide range of applications that use image recognition. A good example of a type of app that uses this technology is one designed for editing pictures.
Other impressive features that have been created as a result of using machine learning technologies include fingerprint recognition, differentiating between male and female individuals, identifying a person’s retina and many other functions.
- Advanced customization
Many companies deal with e-commerce value customer experience. One of the ways to assess the potential for success of a particular app is looking at its ability to be customized.
The machine learning algorithms used in customization enable individuals to customize their apps according to their liking. For example, some apps created for watching movies or sitcoms use an individual’s watch history to suggest similar shows or movies. A good example is Netflix.
- Optical character recognition
This is one of the most useful features developed by machine learning technology. There are several algorithms that make it possible to identify certain important documents, make a translation of words on particular images, credit cards and so on.
It should be noted that text has so many different properties and developing an algorithm for optical character recognition should put all these properties under consideration.
- Sensory data analysis
This feature is one of the most impressive as a result of advances in machine technology. In fact, it has made such an impact it is being used in medicine.
There are so many apps on either Android or iOS platforms designed to record the user’s activity. This activity includes the number of footsteps taken, heartbeat and other important physiological statistics.
- Intelligent data analysis
In intelligent data analysis, machine learning is used in combination with Big Data. Together with Big Data, the machine learning collects vast amounts of information and process it respectively.
Machine learning also learns from this data, which is used to make predictions. Machine learning in intelligent data analysis can be used by companies to increase the knowledge about their audience. This knowledge can be used to create solutions to any problem and make better business decisions in the future.
Machine learning also has its fair share of disadvantages. Here are some of them:
- Amount of data needed
Machine learning requires a lot of data to be processed before a particular algorithm can be able to self-develop.
Machine learning does not come with a guarantee of success. There is no certainty that machine learning algorithms will work in every possible scenario.
- It is non-specific
There is no particular technique designed to obtain an expected outcome. Many techniques need to be experimented with before the right one can be identified.
- Interpretation of particular result
Interpreting the results in assessing the performance of a particular machine learning algorithm is a challenge.
Machine Learning Application Areas
There are many fields that machine learning can be applied to. Some of the most common ones are described below:
- In robotics
Voice command is one of the best features developed by machine learning technology. This feature has enabled robot engineers to develop robots that can be controlled by the voice of the user.
This technology has a wide range of applications, from large factories to family homes. Now individuals can use various apps to control these robots. A good example is the cordless vacuum cleaner, which uses machine learning to adapt to new environments. According to Statista, 7.2% of the robotics industry has integrated machine learning and this number is likely to rise.
- In finance
In finance, machine learning is used to predict any future events that may occur concerning the industry.
A very good example of the use of machine learning in finance is the use of specific software to analyze particular borrowers. Special software can look at any of these individual’s past transactions to determine a credit rating.
- In data mining
In this field, machine learning enables the analysis of data that is usually considered too large to be handled by any other method.
Machine learning makes it possible for a large amount of data to be stored and maintained as well. The analysis of this large amount of data leads to the identification of all possible relationships.
- In e-commerce
E-commerce machine learning applications have also been developed to improve the way online business is carried out. One important machine learning feature that has been fully exploited in e-commerce is customization.
Amazon, eBay, and AliExpress are some of the best examples of companies that have taken full advantage of machine learning for e-commerce apps. For example, a customer will be suggested goods similar to what they usually search for or buy.
Tips for App Developers
Machine learning can be used by mobile app developers to make better apps. Here are a few tips on how to build a mobile app with machine learning:
- The first tip in developing a mobile app with machine learning is to use plenty of data. Results are usually more accurate if a lot of data is made use of.
- When doing the project, developers should also focus on choosing the best possible machine learning method. It is advisable to use a simple one for accuracy.
- Having the right professional when choosing the best possible machine learning approach is crucial.
- Remember, that the data used in creating a machine learning mobile app is significant. Therefore, it is important to make sure the right data is used for app development.
- In order to develop a successful machine learning mobile app, developers must understand all possible data properties.
- Another important point to consider about machine learning for developers is to have enough knowledge of their clients business. A developer who does not understand the business capacity of their employer will not create a suitable app.
- App developers should also test their machine learning algorithms extensively before they can proceed to release any apps to the public.
- Before you create a machine learning mobile app, have a good idea of how much the final product will cost.
The above tips are a good guide for any app developer looking to get a good price for their services and stand out as a developer.
The usage of custom machine learning in apps is growing rapidly. However, it is not safe to say that it has reached full maturity. This technology will continue to expand and more features will be introduced and improved.
Generally speaking, the machine learning technology has made a huge impact on various industries. One of the many industries is online marketing. Many people are choosing to do the most of their shopping online now. This means businesses must focus on creating apps that provide good customer experience.
Machine learning can also be developed for startup businesses who are hoping to make an impact on the online market or other fields. Business owners can hire professional developers and data science team today, just contact us to get more details.