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 Machine learning (ML) is an exciting major of research in computer science and engineering. It is considered a subset of Artificial Intelligence (AI) because it enables the extraction of meaningful patterns from samples, which is a capability of human intelligence. The demand of having a computer that performs repetitive and well-defined tasks is clear: computers will perform a given task consistently and tirelessly, but these tasks would be difficult to accomplish for human. In recent years, machines have showed the ability to learn and even master tasks that were thought to be extremely complicated for machines, showing that machine learning algorithms are potentially useful elements of detection and decision support systems. Another exciting point is the finding that in some situations, computers seem to be able to observe patterns that are beyond human perception. This discovery has led to substantial and increased interest in the major of machine learning in various areas. At a high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a video. The key difference between traditional computer software and machine learning approach is that a human developer has not written codes that instructs the system how to tell the difference between the banana and the apple. Instead, a machine-learning model has been taught how to reliably discriminate between the fruits by training on a large amount of data, in this instance likely a massive number of images labelled as containing a banana or an apple.