||Until recently, the driving paradigm in software engineering has been to design applications for a predefined set of tasks. This approach, however, is quickly being dwarfed by data-driven applications which are designed to use knowledge and understanding to decide how they can best fulfill user needs. Machines are able to gain such an understanding using data. Rather than being programmed with a finite set of capabilities, they are provided with information that they can use to deconstruct their problem space. Through the lens of a practical application, this research emonstrates the foundations of one corner of this growing wave of data-driven engineering-machine learning (ML). Step-by-step, the key principles underlying learning are introduced, supported by a running example of a learning model that was built to uncover insights intrinsic to the New York City Department of Education's (NYCDoE) 2015 School Survey. This paper aims to provide a foundation for scholars from all fields-including and beyond the STEM fields-to understand the principles behind machine learning. As the world becomes increasingly rich with data and dependent on data-driven technologies, it is paramount that innovators are equipped with the knowledge required to conceptualize machine learning. The current era faces problems more challenging in nature than many we have ever seen. True progress against problems as familiar as poverty, and as pressing as climate change, will require both the world's most innovative technologies and the in genuity of experts across all fields. As such, it is the responsibility of researchers to make their work accessible to those external to it; in ensuring that the principles behind tools as powerful as machine learning are truly available to all, we are maximizing our potential to solve problems. This research aims to be an agent of this cause precisely, through presenting the core principles behind machine learning, and by providing a clear platform to enable researchers in their approach to problems both vast in size, and rich in complexity.