The emerging fifth generation (5G) network has the potential to satisfy the rapidly growing traffic demand and promote the transformation of smartphone-centric networks into an Intet of Things (IoT) ecosystem. Due to the introduction of new communication technologies and the increased density of 5G cells, the complexity of operation and operational expenditure (OPEX) will become very challenging in 5G. Self-organizing network (SON) has been re-searched extensively since 2G, to cope with the similar challenge, however by predefined poli-cies, rather than intelligent analysis. The requirement for better quality of experience and the complexity of 5G network demands call for an approach that is different from SON. In several recent studies, the combination of machine leing (ML) technology with SON has been investi-gated. In this paper, we focus on the intelligent operation of wireless network through ML algo-rithms. A comprehensive and flexible framework is proposed to achieve an intelligent operation system. Two use cases are also studied to use ML algorithms to automate the anomaly detection and fault diagnosis of key performance indicators (KPIs) in wireless networks. The effectiveness of the proposed ML algorithms is demonstrated by the real data experiments, thus encouraging the further research for intelligent wireless network operation.