This chapter provides a comprehensive guide to understanding quantum kernel methods, covering the fundamental concepts of classical and quantum kernel methods, their theoretical foundations, and practical implementations. Section 3.1 offers a detailed introduction to classical kernel methods, including their motivation, derivation, and the construction of classical kernel functions. Building on this foundation, Sect. 3.2 discusses the motivation for implementing kernel methods on quantum devices, exploring the potential advantages of quantum kernels. It also introduces the specific implementation of quantum kernel functions, clarifies the relationship between classical and quantum kernel machines, and provides concrete examples of quantum kernels. Section 3.3 delves into the theoretical foundations of quantum kernels, focusing on two key aspects: the expressivity and generalization properties of quantum kernel machines. It examines the diverse feature spaces that quantum kernels can represent and the potential advantages of quantum kernels in reducing generalization error compared to classical kernel methods. This analysis underscores the ability of quantum kernels to improve the accuracy of predictions for unseen data. Finally, Sect. 3.4 demonstrates simple yet illustrative code implementations of quantum kernels using the MNIST dataset.