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Understanding the basics of Machine Learning

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Understanding the basics of Machine Learning

Tech companies are becoming increasingly aware of the possibilities presented by artificial intelligence (AI). 

AI is not a new term for most industry professionals, but their understanding of it is beginning to change as new applications have become more prevalent. What once did little more than provoke visions of human-like robots has now become a much broader term, gradually working its way into both our workplaces and everyday lives. 

Artificial intelligence as a whole has made great strides over the past decade, but it is in the field of machine learning, a subset of AI, where the greatest progress has been made. 

Machine learning enables computers are able to automatically learn something that they were not specifically programmed to do when given access to a data set. This has been a game changer for the countless businesses that have invested heavily in new methods of data collection that enable them to gain valuable insights that were previously unobtainable.

A maturing market​

​According to California-based Grand View Research, the global machine learning market is predicted to grow to almost $100 billion by 2025, which would represent a compound annual growth rate of almost 44%!

 This is because what is known as the ‘digital universe’ – and all of the data contained within it – is growing exponentially. Leading online statistics portal Statista has estimated that the amount of data that we create and copy each year will amount to some 175 zettabytes –175 trillion gigabytes – by the same year. That would mean a tenfold increase over the next decade.

 Another reason for this surge in the market is that more companies are deciding to embark on their own digital transformation, where new technologies such as artificial intelligence are being utilised to make regular business processes quicker, more efficient and more data driven.

 Such a huge increase in the quantity of data being produced has led to more technologies being adopted that can carry out automatic analysis of this data across a wide range of applications and industries.

 Algorithms, driven by the rise in connected AI, are now able to handle tasks and adapt to new information that becomes available, automatically seeking out useful patterns within a data set.

The two types of machine learning

Machine learning is continually evolving. It can largely be divided into two categories: supervised and unsupervised.

 Supervised learning is when a computer is taught to make associations between a set of input variables and their corresponding outputs. This creates the possibility of outputs being predicted based on new inputs.

 Learning is defined as unsupervised when the machine is not fed any output variables and is left to group together and identify relationships between blocks of data without specific direction. Instead of being told what to look for, it spots traits within the data and uses them to form clusters of information based on those traits.

 By learning from experience, modern machine learning algorithms can identify key patterns from large, complex and often unstructured data sets.

Applying the formula

 For a lot of businesses in the early stages of their digital journey, the primary aim in many cases is to obtain useful insights from vast quantities of data. To use a simple example, an ecommerce specialist that wants to find an easy new way to predict its sales for the next year could adopt a machine learning algorithm to carry out this task for it by feeding it data from the previous sales period as well as other relevant information.

 A company that relies heavily on machinery might use machine learning to anticipate when possible equipment breakdown could occur. An algorithm could even help detect fraud by seeking out specific anomalies in a data set.

 Although machine learning is very clever technology, to get the most out of it the data should first go through a process of ‘cleaning’ or ‘cleansing’. In simple terms, this is where data is checked over beforehand to ensure that it’s up to date, with no anomalies that could skew the results or gaps that need to be filled. If data is not ‘clean’ when a program or algorithm is applied to it the outcome will not be accurate and the whole exercise would be effectively pointless.

 This is something that individuals and organisations should be in the habit of doing on a regular basis anyway, even if they’re not currently intending to utilise any form of machine learning or AI.

 Similar to most technologies when they’re fresh and new, machine learning and the wider sphere of artificial intelligence can seem daunting at first. It can be tempting to think of it as something that still belongs in the pages of a sci-fi novel or is limited only to large corporations with vast spending budgets and research capabilities.

 This is a technology that is bringing benefits to businesses of all sizes, from financial services to retail. And while some of the wider potential applications can be considered controversial, such as facial recognition, progress in other areas such as medical diagnosis shows how this could dramatically improve our lives in whole new ways.


Whether it’s for the professional or personal possibilities, machine learning is something that we should all be getting excited about!

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