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Understand the intuition behind Support Vector Machines (SVM), a popular machine learning algorithm. This post explains what SVMs are, their history, how they find optimal hyperplanes with maximum margins, the kernel trick for handling nonlinear data, the math behind the optimization, pros like versatility and cons like complexity, and use cases like bioinformatics, text classification, and time series forecasting. Whether you're new to SVMs or looking for a refresher, this breakdown aims to make this powerful algorithm more accessible.

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