The kernel trick is a popular technique used in support vector machines (SVM) to map high-dimensional data to a lower-dimensional space, making it possible to solve complex problems.

A good resource to learn more about the kernel trick in SVMs is the book "Introduction to Statistical Learning" by Hastie, Tibshirani, and Friedman. The first several chapters of the book provide an in-depth explanation of the theory behind SVMs and the kernel trick, as well as practical examples using Python libraries such as scikit-learn.
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