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Machine Learning - Types & Uses

Updated: Mar 11, 2022

Machine learning is a subset of artificial intelligence (AI) and aids the system in making predictions as well as decisions without being programmed to do so. It is used in a variety of fields such as in medicine, email filtering, speech recognition, and computer vision.

There are four main types of machine learning algorithms. There is supervised, semi-supervised, unsupervised and lastly, reinforcement. In supervised learning, the algorithm is provided by a dataset which includes inputs and outputs and the algorithm makes predictions and is corrected by the operator until it achieves the highest level of accuracy possible. As per contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. The system is then able to draw conclusions from the dataset and is able to describe hidden structures from the given unlabeled data. Semi-supervised machines fall in between as both labeled and unlabeled data is used for training. Reinforcement machine learning algorithms require a reinforcement signal to determine which action is the most suitable.

By combining machine learning with AI technologies, for example, by subjecting additional ML driven checks on tasks, there is a greater improvement in accuracy as well as efficiency. The use of ML can also make it even more effective in processing large volumes of information. Hence, it can be seen that Machine Learning algorithms form an important and irreplaceable basis of AI as we know of it now.

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