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The shortest distance from the separating hyperplane to the nearest test observation.

SVM Margin

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Why does SVM use margin maximization?

When the training data are linearly separable, there are infinite separation hyperplanes, which can correctly separate the two kinds of data. The perceptron uses the misclassification minimization strategy to obtain the separation hyperplane, but there are infinite solutions at this time. Linear separable support vector machine uses margin maximization to obtain the optimal separation hyperplane. At this time, the solution is unique. On the other hand, the classification result generated by the separated hyperplane is the most robust and has the strongest generalization ability for unknown instances. We can take this opportunity to explain the relationship between geometric margin and functional margin.

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