The purpose of this chapter is to provide the necessary materials for determining the optimal introduction of machine learning (ML) processes at different grade levels, with the goal of enhancing students’ understanding of AI and ML concepts. Due to the intricate nature of the ML process, AI education for children often simplifies certain aspects, primarily focusing on imparting AI knowledge. However, concealing the complexities of the ML process can lead to students misunderstanding it. Therefore, it is essential to meticulously unveil the intricacies of the ML process, aligning with the capabilities of preschoolers, elementary school students, middle school students, and high school students. This chapter offers a comprehensive explanation of AI education tailored to students of various grade levels, ranging from preschool to high school, based on findings obtained through empirical research conducted between 2018 and 2023. It delves into the integration of ML concepts into the educational curriculum and elaborates on the strategies used to achieve this integration. Additionally, by emphasising a white-box approach through decision trees, this chapter provides a more accessible and transparent pathway for K–12 students to grasp fundamental ML processes, compared to more complex black-box models such as neural networks. For example, the chapter explains that the ML process can be introduced progressively, with perception addressed at grades K1–3, data representation and reasoning based on if-then rules covered in grades K4–6, and the learning and evaluation of simple ML models introduced at grade K7 and beyond.