Machines Can Learn, But How?

Many experts predict that machine learning (which any companies are currently investing in significantly) will be responsible for the most important breakthroughs in history. That includes being more important than the industrial revolution or the introduction of electricity, the computer, or the internet. Only time will tell whether these predictions prove to be correct, but machine learning is undoubtedly advancing at a significant pace.
What is a Machine That Learns?
Machines Can Learn, But HowA standard machine is programmed to do a particular task while a machine that can learn is programmed to learn how to do it. This learning is achieved through data, so the quality of the machine is dependent on the data.
In an article on Datanami, authors Hui Li and Fiona McNeill explain that machines learn in four main ways:
·         Supervised learning – labelling the data that the machine uses to learn as well as defining the desired output.
·         Semi-supervised learning – this uses some data that is not labelled and some data that is labelled.
·         Unsupervised learning – the data used by machines that learn unsupervised is completely unlabelled. In this form of learning, the machine looks for patterns in the data.
·         Reinforcement learning – this is a trial and error method of learning. Machines that learn in this way will try a scenario, get feedback from its environment, then adapt its approach based on that feedback.
Machine Learning’s Various Forms
Machine learning is a term used to describe a number of different types of technology, all of which learn in one or more of the ways listed above:
·         Artifici

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