This paper introduces FDILAB (False Data Injection LABoratory), an open-source educational tool tailored for power system cybersecurity and machine learning experiments. It offers an intuitive interface, accommodating users of various computer skill levels and bridging the gap between power systems and data science expertise. In addition to a brief explanation of the application architecture, we showcase a set of FDILAB's capabilities by using it to address an anomaly detection problem relevant to power system cybersecurity and machine learning. The demonstration includes data generation, developing and training a well-known machine learning model, and testing it within the FDILAB environment While room for improvement remains, we now consider the application suitable for classroom use and plan to include it in a future course for master's students in power systems engineering.