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2.5.1 Types of Machine Learning

Machine learning is divided into two main categories. Figure 2.8 illustrate the
difference between supervised and unsupervised learning.

Figure 2.8 Supervised and Unsupervised learning
(Source: Saini, 2021)

i. Supervised Learning

In supervised learning, the algorithm is trained on labelled data, which

means that each piece of input data has a matching piece of output data
and are classified accordingly. Based on the training data, the algorithm

learns how to map inputs to outputs and can then predict the outcomes
of fresh, unforeseen data. For each concept being learnt during

supervised learning, numerous training examples are needed. (Roads,
2019).

ii. Unsupervised Learning

Unsupervised learning is a subset of machine learning in which the
algorithm is trained on data that has not been labelled, i.e., the outputs

do not have matching labels (Roads, 2019). Without any assistance or
feedback, the algorithm learns to recognize patterns or structures in the

supplied data. For tasks like grouping, dimensionality reduction, and

anomaly detection, unsupervised learning can be applied. When there
are numerous concepts to learn, unsupervised learning may be simpler

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