This chapter establishes the foundation for network machine learning. We begin with network fundamentals: adjacency matrices, edge directionality, node loops, and edge weights. We then explore node-specific properties such as degree and path length, followed by network-wide metrics including density, clustering coefficients, and average path lengths. The chapter progresses to advanced matrix representations, notably degree matrices and various Laplacian forms, which are crucial for spectral analysis methods. We examine subnetworks and connected components, tools for focusing on relevant network structures. The latter half of the chapter delves into preprocessing techniques. We cover node pruning methods to manage outliers and low-degree nodes. Edge regularization techniques, including thresholding and sparsification, address issues in weighted and dense networks. Finally, we explore edge-weight rescaling methods such as z-score standardization and ranking-based approaches. Throughout, we emphasize practical applications, illustrating concepts with examples and code snippets. These preprocessing steps are vital for addressing noise, sparsity, and computational challenges in network data. By mastering these concepts and techniques, readers will be well-equipped to prepare network data for sophisticated machine learning tasks, setting the stage for the advanced methods presented in subsequent chapters.