There is an increasing demand for expert analysis of cybercriminal communities. Cybercrime is continually becoming more complex due to the rapid development of digital technologies, on the one hand, in new types of criminal activity, such as hacking, distributing malware and DDoS attacks, and on the other hand, in digitised forms of more traditional crimes, such as email scams, phishing, identity theft, and cryptographically secured black markets. Tackling this broad array of behaviour requires tool support for multi-disciplinary investigations, and a connecting framework that can adjust flexibly to changes in the populations being studied. In this work, we present AMoC, a multi-faceted machine learning toolkit that combines structured queries, anomaly detection, social network analysis, topic modelling and accounts recognition to enable comprehensive analysis of cybercriminal communities and users. The toolkit enables the extraction of findings regarding the motivations, behaviour and characteristics of offenders, and how cybercriminal communities react to interventions such as arrests and take-downs. In our demonstration, the toolkit is deployed to analyse over 150,000 accounts from 35 underground marketplaces.