Machine learning – what is it and why?
Tech Industry
March 21st, 2023

Machine learning (ML) is a type of artificial intelligence (AI) that enables software applications to predict outcomes more accurately without being explicitly programmed to do so, by using historical data. Algorithms analyse large amounts of data to make recommendations and predictions based on their data input – almost like superpowered statistical analysis.

Machine learning is intended to minimise human intervention as software apps identify trends and sequences with which they solve problems, make decisions, and predict conclusions independently.

From a business perspective, because it can highlight trends in operational patterns or customer behaviour, ML can be used, for example, to detect fraud and cyber threats, predict maintenance requirements or recommendations (just think of Uber route predictions or Amazon recommendations) in addition to product development.

The way that algorithms learn to improve the accuracy of predictions can be divided into four: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning – with each chosen for the type of data to be predicted.

Firstly, in high-performance supervised learning, the algorithm is given specified inputs and outputs of defined variables. This is suitable for dividing data between two categories, picking between more than two types of answer, predicting continuous values or producing the most accurate prediction from multiple other predictions.

Secondly, in high-efficiency unsupervised learning, the algorithms train on unlabelled data, looking for connections and grouping data into subsets. It is suitable for identifying similar groups of data, identifying unusual groups of data, and identifying groups often occurring together, as well as variable reduction in the data set. Algorithms train on predetermined data and the predictions or recommendations they output.

Semi-supervised learning, combines both supervised and unsupervised learning in that data scientists provide labelled data inputs then the algorithms scout the data, develop independent insights and apply them to unlabelled data to form independent outputs, combining the performance and efficiency of supervised and unsupervised learning. Uses might include linguistic translations or fraud detection.

The fourth category is reinforcement learning which is used in multi-step processes within set guidelines. The algorithm has a clear task or goal and a specified route to get there which is informed by ‘rewards’ and ‘punishments’ but it decides subsequent actions itself as it either approaches or recedes from the goal. Used, for example, in gaming or robotics.

Facebook, Amazon, healthcare and autonomous vehicle manufacturers all use ML to recommend actions or reinforce behaviours, adjusting responses accordingly. Business applications can include CRM systems, web analytics for SEO and marketing, HR filtering for job applications or virtual assistants.

For autonomous vehicles, machine learning can be used to predict movements or recognise patterns and objects as sensors and cameras collect data on the vehicle’s surroundings which is used to make decisions on what actions the vehicle performs.

In healthcare, an example of machine learning is in using predictive analytics to forecast patients’ responses to treatments with a high degree of accuracy or enable patients to have various vital signs monitored in real-time, but remotely.

Machine learning is used in cybersecurity through biometrics, for example in facial recognition identity verification, or in banking by processing vast numbers of transactions almost instantaneously to spot unexpected customer behaviour or irregularities, or by detecting suspicious activity which may be an indicator of fraud and can subsequently be scrutinised and followed up – reducing the risk of data breach, money-laundering or cyber-attack.

And these are just a few examples.

Machine learning will become increasingly prevalent in our daily lives. Its value throughout a wide range of industries lies in the potential to make more accurate predictions and business decisions – which means it will continue to be incorporated into changes in business operations. The future of machine learning will open up many opportunities for businesses, driving innovation and development, and enhancing a wide variety of processes.

ML and AI is here to stay.

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