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Why machine learning matters for business

The key advantage of machine learning is that it enables computers to access hidden insights, finding patterns that can either be used by researchers to find hitherto unknown patterns (as might be used for drug discovery) or by businesses to find insights into customer behaviour or to target potential new consumers.

Machine learning not only helps find things that people may not, it also does what people do far more quickly. Machine learning algorithms tend to operate at expedited levels.

What is machine learning?

Machine learning refers to an automated means of assessing data to discover patterns by training a model, so that the ability to spot patterns and interpret data improves over time. The models are computer-driven and they require the use algorithms, and it is the algorithms that drive improvement from experience.

Machine learning can mean slightly different things in different contexts and it is interdisciplinary in nature, drawing on techniques from diverse fields like computer science, mathematics, statistics and artificial intelligence.

Essentially, machine learning is about automated predictive analytics. This encompasses a range of statistical techniques, such as predictive modelling, to analyze current and historical facts to make predictions about future or otherwise unknown events.

Supervised and unsupervised machine learning

Machine learning can be ‘supervised’, where a data scientist is needed to provide input and desired output during algorithm training. In contrast, unsupervised algorithms use an iterative approach termed deep learning, whereby the algorithm can review the data and derive conclusions. Unsupervised learning algorithms are called neural networks, and they tend to be used for more complex processing tasks.


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What is the difference between machine learning and artificial intelligence?

Simply put, machine learning is a subset of artificial intelligence in the wider field of computer science. This means artificial intelligence is the broader concept of machines being able to carry out tasks in a way that is “smarter” and more intuitive than machines of earlier generations.

Everyday examples

A simple example of machine learning appears when browsing on the Internet. Often the adverts that appear relate to previous purchases or interests. This occurs because recommendation engines deploy machine learning to personalize online advert delivery. These tends to happen in real time.

A woman shopping online

A woman shopping online
Keith Williamson, Flickr (CC BY 2.0)

More sophisticated applications of machine learning include insurance platforms that can spot fraud detection; the use of spam filters that ‘learn’ how to spot and detect scams as spammers become more sophisticated; devices that undertake network security threat detection, which need to learn how to detect the latest viruses; predictive maintenance in the engineering world; and also with building news feeds, in terms of sending tailored content. A well-known example is Facebook’s news feed.

More sophisticated examples of machine learning, which require the use of unsupervised learning algorithms, include image recognition, speech-to-text and natural language generation.

Business concerns

A concern of businesses is how to handle machine learning applications. According to a overview on the website Chief Executive, many companies do not have staff who are experienced in handling machine learning in their employ. Moreover, not all machine learning projects will be a ‘success’, especially when measured against the common business criteria of increasing sales and decreasing costs.

A further problem is that although there are machine intelligence solutions, a great volume of data is not accessible, sizable, usable, understandable, or maintainable, in terms of being usefully processed by artificial intelligence.

What makes for good machine learning?

Machine learning can be made to work for business if it is implemented carefully, with a clear plan and direction. Effective machine learning requires skilled and competent personnel. The algorithm needs to have data preparation capabilities; to work on an iterative processes; and they should be ensemble modelling. The system needs to be semi-or fully automated, with future scalability.

Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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