Machine learning has become one of the most important tools for digital systems. There are several reasons for this importance. The first one is that machine learning adds intelligence to the system. Hence, the system can make decisions, predict future value of a variable, or apply data grouping. On the other hand, machine learning methods (especially neural network-based ones) depend on high computation power and memory requirements. Fortunately, recent advances in embedded systems (especially microcontrollers) allow machine learning methods to be applied to these devices. Therefore, more intelligent embedded systemsEmbedded system can be developed. This book aims to cover fundamental machine learning methods those can be implemented on embedded systems (more specifically microcontrollers) via their practical usage. To do so, we will make a brief introduction in this chapter. Hence, we will start with explaining what machine learning is. Then, we will cover different embedded systemEmbedded system types. Since these have their specific structures, we pick the STM32F746NG microcontroller based on Arm® Cortex™-M architecture as our embedded systemEmbedded system in this book. Afterward, we will discuss how machine learning can be realized on microcontrollers and what are the advantages and challenges while performing this operation. Finally, we will provide a brief overview of the book including its general layout and chapter-based content. Hence, the reader can judge what to expect from the book.