This chapter introduces the emerging field of quantum machine learning (QML), which aims to integrate the strengths of quantum computing with classical machine learning to achieve computational advantages. It begins by contextualizing QML within the broader evolution of computing paradigms, from CPUs and GPUs to quantum processors, and highlights the limitations of classical systems in handling increasingly complex learning tasks. The chapter then presents a first glimpse into QML, including the foundational components of quantum computers, different measures of quantum advantages, and the main research directions in QML. Progress in QML is reviewed under two regimes: fault-tolerant quantum computing (FTQC) and noisy intermediate-scale quantum (NISQ) devices. Finally, the chapter outlines the structure of the tutorial and explains its intended audience. Together, these discussions provide a comprehensive background and motivation for the chapters that follow.