Machine Learning

What is Machine Learning?

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.  Machine learning focuses the development of programs and apps that change when exposed to new data. This discipline reports such sciences as mathematical statistics, analysis, and optimization techniques.

The process is similar to data mining, as both are primarily aimed for looking through large amounts of data to try and find a pattern. The major difference is that data mining is to find patterns which humans can see and make a decision, while machine learning is finding these patterns and then the computer deciding what to do on its own.

A machine learning algorithm is normally classified as either supervised or unsupervised. In the case of supervised algorithms, the program is given ‘training data’, which the program then created an inferred function used for mapping new examples. A commonly used example of this is handwriting recognition. An unsupervised algorithm will try to find correlations without any external inputs other than the raw data, for example trying to group together similar pictures.

Real World Uses in App Development

  1. Face detection: Snapchat’s filters
  2. Face recognition: Automatic tagging in Facebook
  3. Image classification: Google image search
  4. Speech recognition: Siri and Alexa being able to respond to your voice
  5. Anti-spam: Spam filters have learnt from years of data to stop spam being delivered to your inbox.
  6. Recommendations: Your Facebook news feed is specialized for you based on your previous likes and views.
  7. Weather forecast: Machine learning is applied in weather forecasting software to improve the quality of the forecast.


The Future

Machine learning’s possibility are vast and in the future will cover huge amounts of our personal lives. We could gradually get rid of many routines that are needlessly consuming valuable time.

Some areas that will be improved due to machine learning:

  1. Self-driving cars: Although this technology is available in part, in the not too distant future cars will be completely self-driving. Self-driving cars have already shown to be safer and have fewer crashes than their human counterparts, and this is only going to improve.
  2. Healthcare: Inspection, diagnosis and treatments will all be possible without actually having to see anyone. A well-developed algorithm with enough data will be able to determine what’s wrong with you quicker and more reliably than a person.
  3. Law: Machine learning will lack the creativity of some of the top lawyers, but in the majority of cases (if the data was made available), an algorithm could create a defense based on previous similar examples.
  4. Accounting: Accounting could be boiled down to someone refining algorithms for law changes and reviewing the output, there would be no need for any of the manual work currently done.


Machine learning is becoming a large part of our lives, while remaining virtually unnoticed in everyday use. We believe that such technologies allow evolving, making everyday life easier, pushing technology progress forward.

The next stage is to ensure security on the network, by more accurately filtering unwanted or inappropriate content, and ensuring safety on the roads, at airports or other places.

For general mobile development you should consider more accurate geolocation, recommendations of places to relax or companies providing different kinds of commodities and services. This will become increasingly important as more and more companies inserting elements of machine learning into the code.

Related Projects

Out Tonight App

Out Tonight is a mobile application allowing for instant communication between nightlife venues with people in their immediate vicinity. This app is perfect for people travelling around the country and wanting to find somewhere to eat, drink or even do a bit of karaoke.

Related Services

You may also like:

The Importance of a Prototype

A prototype is an initial version of your potential final product. Learn why they are important and whether it should be used in your next project.

Objective C vs Swift. Which platform should you use for your next application?

Developers programming for iOS currently have two choices for creating an App, Objective C and Swift. While Swift is seen as the 'offspring' of Obj C there are many differences. Learn about them here.

Importance of offline compatible apps

Here we explain the benefits of an offline first approach to app development.

How to make your next app idea a success

With over 5 million apps online today, more than a good idea is needed to create an app that people will want to use. We discuss a few things to consider before launching your next project.

5 Reasons to invest in a mobile application

Here we look at reasons how you could possibly save your business some money as well as improve customer engagement.

Waterfall vs Agile Development Processes

Learn the differences between the two most popular methodologies in development today and the benefits of each. See if you have any preference on which you'd rather use on your next project.

Social media and it's importance to your business

Are you undecided on whether your company needs social media? Read on and we will try to convince you as to why it's essential.

Analytics within mobile apps

See how analytics are just as important within mobile apps as your website.

Choosing a Mobile Development Platform for your App

Native vs Cross Platform App Development. Read ahead and decide which you think would be most beneficial for your business

User Interface Design in Mobile Apps

Find our how a great user interface can lead to happy customers and better user retainment

Business Mobile Applications

How in-house mobile apps can give your business an efficiency boost.