This chapter introduces the network machine learning landscape, bridging traditional machine learning with network-specific approaches. It defines networks, contrasts them with tabular data structures, and explains their ubiquity in various domains. The chapter outlines different types of network learning systems, including single vs. multiple network, attributed vs. non-attributed, and model-based vs. non-model-based approaches. It also discusses the scope of network analysis, from individual edges to entire networks. The chapter concludes by addressing key challenges in network machine learning, such as imperfect observations, partial network visibility, and sample limitations. Throughout, it emphasizes the importance of statistical learning in generalizing findings from network samples to broader populations, setting the stage for more advanced concepts in subsequent chapters.