The age of generic services is dropping off. Today's customers expect more, and they want you to produce software tailored to their needs. This can be done with machine learning. Its solutions are a revolution happening right before our eyes, and those late to the party might find themselves at a huge disadvantage.
Simply put, Machine Learning is a subset of AI algorithms (see the difference between AI and ML) – it is the way of learning that enables machines to evolve. As a result, computers that use machine learning systems can recognize patterns in enormous datasets and act on them.
But how does machine learning work?
Apparently, Googlers say that machine learning mechanisms are easy. So let’s have a closer look to see if they are really that simple.
Modelis the system that makes all the predictions and identifications.
Parametersare the factors used by the model to form its decisions.
Learneris the system that adjusts the parameters and revamps the model by looking at differences in predictions versus actual outcome.
Imagine you are a beginning team leader. Now, let’s translate this into a real-life case.
When you are just starting, you need to determine the optimal time to complete a task within your team. First, you observe how long certain tasks take, and then you learn over time what the sweet spot is for each task. Once you become a more experienced team leader, you simply know how long things take.
This example can play the role of a very simplified demonstration of the process. Now let’s plug the machine learning technology into it. In practice it looks like this:
First of all, the model that makes all the predictions needs to be applied to a computer (by a living breathing person), and then, this model needs to be programmed accordingly in order to study the particular activity that’s integrated into the system, such as the time for task completion from the example above.
Such a model relies on parameters to evaluate what the optimal time for the completion of a task is. You can imagine that they look as follows (but may differ between tasks):
1 hr - 30%
2 hr - 50%
3 hr - 70%
4 hr - 100%
This data applied to the machine learning system is usually called the ‘training set’ or ‘training data’, and it’s used by the learner to align the model and continually improve it. Also, the learner can rework predictions depending on the different results it records over time. It makes very small adjustments to the parameters to refine the model.
Machine learning uses a mathematical equation to define all of the points above. So this is how the trend is formed – the computer can make accurate predictions over time and interpret real-life information.
How to use machine learning in your app?
The number of businesses investing in machine learning is expected to double in the next three years. Also, 75% of US companies have exceeded their sales targets thanks to machine learning. As a result, we may assume that investing in machine learning is quite lucrative now.
It’s no wonder that machine learning can bring some substantial benefits to your business. It can streamline a wide range of tasks and significantly improve the user experience of your product.
1. Personalised experience
Machine learning systems allow you to customize the user experience of your application.
Through machine learning, your solution gains the capability of approaching users individually, and it can meet their expectations better. Customization lets you match the most relevant content to your users based on their individual interests and classify users based on their preferences and collect user information.
This is how you improve user experience!
2. Advanced search
Machine learning isn’t just a system that provides clever marketing. It also offers advanced search performance with cognitive technology.
While various analytical apps collect more and more data, machine learning lends a helping hand when a user is searching for specific information.
This system allows you to optimize search in your application, so it can deliver better and more contextual results, and make searching faster, more intuitive, and less troublesome for your customers.
Machine learning algorithms pick up and study all the queries your customers type into the search box and then they prioritize answers according to what was most useful to a specific user.
One of the best examples here is Reddit, which uses machine learning systems to improve search performance for hundreds of millions of its community members.
3. Better security
Machine learning can also help you determine who should have access to your solutions. This will allow you to strengthen the security of your app's authentication. Now, your customers can use any kind of biometric data, such as their face, voice, or fingertips to make their tools safe.
The application of machine learning to security aspects can be seen in today’s iPhones. For instance, the iPhone X offers face-authentication as one of the ways in which you can unlock your device. This is an efficient way to secure the system from being unlocked by someone other than you. And it works really well!
Improving security with machine learning is a smart decision for any kind of mobile app.
But it doesn’t end here! Machine learning has a wide range of uses. It can be employed as a recommendation system for related products in apps. This is widely used, for instance by Netflix and Spotify, to provide you with customized entertainment.
It also works as an engine for Instagram’s news feed discovery and Airbnb's forecasting to predict when you are going to book in the future and what kind of property you would like.
Machine Learning Systems can enhance your app with a clever personalization engine and cutting-edge search mechanisms, thus boosting your sales. To get a bigger picture of pros and cons of Machine Learning read our blog article.
As you can see, Machine Learning can be helpful in any kind of application. The best part is that this market is growing every year!
So why not make a decision and apply it to your business?