Machine Learning In Applications – 4 Examples of how it is used

Nowadays machine learning services are among the most popular topics and an integral part of our everyday life. We are sure that you are using Machine Learning applications on a daily basis. Even those people who know nothing about machine learning itself, they experience it every day!

For instance, Netflix recommends shows and movies to its subscribers based on past history and preferences. This system works thanks to the machine learning algorithm, and in 2017 it saved Netflix $1 billion[1]. This fact is impressive but to reach positive results, it is essential to understand what machine learning is and how exactly it works. We are here to answer both of these questions and even more.

Machine Learning: What is it?

The first thing you should discover about machine learning is that it is not an alternative term for artificial intelligence. So, artificial intelligence is a specific area of computer science, which implies the development of machines with an ability to work and react just like humans do. In turn, machine learning is a branch of artificial intelligence and data analytics techniques. It is based on the concept that systems can learn from experience and new data, identify specific patterns, and make decisions, while the human intervention is minimized.

We have already mentioned Netflix and its recommendation engine, but there are many more examples of how machine learning can be used. It is a great tool in such areas as natural language processing (for voice recognition apps), image processing and computer vision (for object detection, face recognition, and motion detection), manufacturing (for predictive maintenance), retail (for demand prediction, business recommendations), healthcare (for analyzing patients’ data), and so on. Fraud detection and prevention, and predictive analytics are other things machine learning often helps to deal with. In a few words, machine learning is a great solution when the issue involves loads of data and too many variables.

What else should we expect? We are not able to predict the future. For now, we can say that business intelligence for small business will be constantly implemented in more and more, even in medium and big companies. Machine learning algorithms in order to decrease their expenses and gain more profits. Apart from this, the application of machine learning in the healthcare industry is also going to increase. Virtually any area can be “improved” with this technique, and more and more of them are expected to join the evolution: education, agriculture, jurisprudence, and many more.

You may also find it interesting – Machine Learning and Deep Learning.

How Machine Learning works

In short, to develop a machine learning system, you have to do the following things:

• Collect the data
• Prepare the data
• Choose a model (for instance, a linear one)
• Teach the model (this process is usually called “training”)
• Evaluate the model
• Fine-tune the parameters, if necessary
• Get the results

Everything seems to be pretty simple, but it is not really like this — you may have to collect a lot of data even if you want to build only a self-learning chatbot. That’s why it is better to find a team of experts to deal with your machine learning challenge in case you have any doubts or don’t have enough experience. However, we still want you to know how machine learning works, at least superficially.

So, machine learning applications use a few types of techniques. Supervised and unsupervised learnings are the most widely used of them, followed by semi-supervised and reinforcement learnings. Here are some details about each of them.

Supervised machine learning

To train a model, this algorithm uses a set of input data and already known responses to this data. As a result, the model makes well-founded predictions when the new data arrives. Supervised machine learning is usually chosen when there is historical data available to predict possible future events.

Unsupervised machine learning

The goal of unsupervised machine learning is to check the input data and find a structure within it — this technique is used in case the data has no labeled responses. This technique is a popular choice for dealing with transactional data.

Semi-supervised machine learning

The semi-supervised technique is pretty similar to supervised machine learning, but it still has a specific feature — for training a model, it uses both labeled and unlabeled data. Whereas, the amount of unlabeled data is much larger than the amount of the labeled one. Semi-supervised learning is usually chosen in case expenses associated with labeling the data are too high to make a fully labeled training process possible.

Reinforcement machine learning

This technique implies going through the process of trial and error — in this way the algorithm understands which actions lead to the most significant rewards, and learns the most efficient policy. Reinforcement machine learning is often used in gaming and robotics industries.

Read more about Machine Learning Models.

How you can use Machine Learning in your App

Machine learning is used in numerous areas, and mobile applications are not an exception. Here are some goals you can reach using machine learning in your own app.

1. Personalized experience

Thanks to the continuous learning process, you can get an opportunity to classify and structure your users, and, therefore, develop an individual approach to each of them. For instance, Uber Eats informs users about the estimated delivery time, and this estimation is rather precise, as it is based on real-time traffic conditions. Netflix can serve as an example here as well.

You may find it interesting – Machine Learning Use Cases for Retail and eCommerce.

2. Improved security

This goal usually means implementing a voice or face (image) recognition. It is not the easiest task ever, but the security of your users will grow significantly, just like their loyalty. The BioID app is a great example here — is offers quick and simple multi-factor authentication with mobile face recognition. With its help, users can log in to any websites or applications which support BioID.

3. Entertainment

There are numerous apps on the basis of machine learning which have only one mission — to entertain their users. For instance, Snapchat allows users to update their pictures with special filters.

4. Better search performance

This would be especially useful in case your product contains loads of data — if you simplify the search process, it would be easier for users to experience your application. Therefore, they will be more likely to stay loyal. To reach this goal, you can equip your product with the voice search and spelling correction features. Search by image is another option, and Pinterest Lens is a nice example here.

Find out more about Natural Language Processing Solutions.

VC backed startups and machine learning in applications

Startups with machine learning get more VC money than those ones which have nothing to do with all the branches of artificial intelligence, and this article successfully proves this statement. So why not join them and lead your own company to the place under the sun? Now you know some important details about machine learning, so it would be easier for you to reach your goal. However, if you still have any questions, you know where to find us.

Read more about ML technology: Machine Learning In Marketing.

Also, see our machine learning services to find out more.

References

[1] Statwolf. 9 key facts about machine learning in 2017. Nov 6, 2017. URL: https://blog.statwolf.com/9-key-facts-about-machine-learning-in-2017. Accessed  Apr 25, 2019.