Abstract
In this chapter, we investigate Support Vector Machines (SVM) for both linearly
separable and linearly non-separable cases, emphasizing accessibility by minimizing abstract
mathematical theories. We present concrete numerical examples with small datasets and
provide a step-by-step walkthrough, illustrating the inner workings of SVM. Additionally, we
offer sample codes and comparisons with the SVM model available in the scikit-learn library.
Upon completing this chapter, readers will gain a comprehensive understanding of SVM's
mechanics, and its connection to the implementation and performance of the algorithm, and be
well-prepared to apply it in their practical applications.
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