This chapter presents a comprehensive workflow for applying network machine learning to functional MRI connectomes. We demonstrate data preprocessing, edge weight transformations, and spectral embedding techniques to analyze multiple brain networks simultaneously. Using multiple adjacency spectral embedding (MASE) and unsupervised clustering, we identify functionally similar brain regions across subjects. Results are visualized through abstract representations and brain-space projections, and compared with established brain parcellations. Our findings reveal that MASE-derived communities often align with known functional and spatial organization of the brain, particularly in occipital and parietal areas, while also identifying regions where functional similarity doesn’t imply spatial proximity. We illustrate how network machine learning can uncover meaningful patterns in complex neuroimaging data, emphasizing the importance of combining algorithmic approaches with domain expertise to motivate the remainder of the book.