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Machine Learning
Machine learning is a field of artificial intelligence that empowers computers to learn and make predictions or decisions from data without being explicitly programmed. It involves the development of algorithms and models that use data to allow machines to recognise patterns and relationships, make predictions, and generate insights.
Whether it’s recognising images, recommending products, analysing text, or optimising processes, machine learning leverages the power of data-driven learning to enhance automation, decision-making, and problem-solving in a wide range of applications across industries. Through the iterative process of feeding data into these models, adjusting parameters, and refining the algorithm’s responses, machines can continuously enhance their performance and accuracy over time.
Why do you need to use machine learning
Businesses should integrate machine learning into their operations to unlock unprecedented insights from vast amounts of data. By leveraging machine learning, businesses can gain a competitive edge, enhance productivity, optimise operations, and ultimately drive growth and innovation in a data-driven world.
Application areas
Machine learning can be used in different ways including:
- Regression: The prediction of numeric values given other input data. Use cases include the prediction of sales volumes, costs, inventory levels, accounts payable collections, cash balances and other numeric values within a business.
- Classification: Classification works in a similar manner to regression, however instead of predicting numeric values, classification algorithms predict which class or category a certain item should belong to. Examples include predicting whether a potential customer will, or will not buy a product; whether a debtor will pay or default on outstanding debt; whether an equipment will fail or not; and many others.
- Clustering: Clustering groups data points or items based on similarities. Clustering algorithms can find similarities that would be difficult to find using the naked eye and is thus an effective way of creating groups in the absence of prior knowledge about potential similarities. The most popular use case for clustering is the determination of customer segments based on customer behaviour and other customer traits.
- Anomaly detection: Anomaly detection is a special form of machine learning that looks to find anomalous data points or outliers. Use cases include fraud detection, identifying cyber security events, and abnormal operating conditions for machines and equipment.
- Root cause analysis: Root cause analysis works hand in hand with anomaly detection to uncover the root causes of observed anomalies. Use cases include unpacking process failures, and understanding the causes of control breakdowns.
- Personalisation: This involves the creation of personalised experiences for users such as customers or employees based on their behaviour. A use for personalisation is showing different website content to different users based on how they have interacted with the site in the past.