Machine learning in mobile apps | Does your app need it?
Have you ever been sat in traffic and wondered how Google Maps can predict the quickest route for you? Alternatively, have you ever wondered how Facebook automatically tags you on your friend’s photo? Well, the answer is simply Machine Learning (ML).
All these businesses use ML in their mobile apps to do a lot of the work for them. As well as to improve the user experience and most importantly, to reduce lifetime costs.
In this blog post we will go through a few things:
- Firstly, what is ML and how does it work?
- Secondly, the different types of ML
- Thirdly, what are the benefits of ML technology
- Finally, some real life examples of this technology
So… What is Machine Learning?
Machine learning, simply put, is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience in order to make better decisions.
The learning process starts with observation of data, such as examples, direct experience, or instruction, all whilst looking for a pattern in the data. The main aim of ML is to allow computers to learn automatically without the need of human intervention. Thus saving in human resources and cost.
Types of Machine Learning
Machine learning can be generally categorised as supervised and unsupervised. Below we show the main four types of ML and how they are used:
Supervised machine learning algorithms
Supervised ML is where the humans act as the teachers. We feed the computer with training data containing the predictors (input) and then we show it the right answer (output). This way, allowing the computer to learn the patterns and learn.
Unsupervised machine learning algorithms
With unsupervised ML there is no teacher. The computer learns via unlabelled data and the patterns it finds. This method is extremely useful if the person doesn’t know what to look for in the data.
Semi-supervised machine learning algorithms
Semi-supervised learning falls in between these two. In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building.
Reinforcement machine learning algorithms
As the name suggests, in this type of ML, the human must provide the computer with simple feedback to guide the machine learning process. This is less time consuming than supervised but still has human interaction as apposed to unsupervised.
Benefits of using Machine learning in mobile apps.
One of the biggest benefits of ML in mobile apps is personalisation. Based on shopping patterns for example, businesses can get insights into customer behaviours, likes and dislikes. Using this information, businesses can then send users messages, emails, etc based on the learnings.
Advanced product search
ML can analyse customers’ queries and use the information to prioritise the results that matter the most to them.
Fraud Control and Security
ML can help improve security arrangements and fraud control systems. They can analyse the behaviours and detect all kinds of irregularities to identify threat or a fraud. This type of machine learning is used a lot by banking apps.
As you might have already figured out by now, ML is basically just learning behaviours or patterns. Therefore, if you can learn the behaviour of your audience, clients or competitors then you will be able to forecast trends. Whether it be product, behavioural or other.
ML has many other benefits and uses that we have not listed here. To find out more benefits of ML don’t hesitate to get in touch.