Understanding Machine Learning

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Machine learning is a form of artificial intelligence that empowers computers with the ability to learn without being explicitly programmed. Machine learning, in other words, can be defined as a subfield of computer science that focuses on the development of computer programs which can actually teach themselves to change, grow and modify when exposed to new sets of data.

Machine learning is related to data mining. Both of these systems carefully search through massive pools of data to find strategic patterns. However, the difference is machine learning not only detect patterns but it then modifies and adjusts program actions. As such, it self-teaches to evolve and mature when exposed to data.

Facebook’s News Feeds, for example, leverage machine learning to personalise each of its member’s feeds. So if you stop scrolling in order to read, comment or like a specific friend’s posts, Facebook News Feed will show you more of that particular friend’s activity in your feed.

Facebook can do this because the software behind its News Feeds simply uses statistical analysis and predictive analytics to detect patterns in your browsing data and then utilises the findings of these patterns to populate your News Feed. If you stop reading or liking that friend’s posts, this new behaviour or pattern will be included in the data set and your News Feed will be updated accordingly.

Another example is Jaguar Land Rover New cars built by Jaguar Land Rover have around 60 on board computers that produce a massive 1.5 GB of data daily across over20,000 specialised and defined metrics. Engineers at the company leverage machine learning to analyse the data and understand how customers actually behave with the vehicle. Machine learning helps engineers design better vehicles which are tailored according to their customers’ personalised requirements. Also, and perhaps more importantly, by utilising and harnessing the true power of the data,designers can predict potential safety issues with their cars.

Why Is Machine Learning So Important Today?

Machine learning has become popular due to a range of factors such as growing volumes and varieties of data, affordable computational processing and more secured data storage. Because of this, it is now possible to securely and automatically produce models that can analyse bigger and more complex data and deliver faster, precise and accurate results.

To learn more about how machine learning, data science and hyper personalization can help your business, please visit Quantiful‘s website.


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