This innovative practice work-in-progress paper explores the outcomes of a Machine Learning module taught to undergraduate students from different majors with various levels of programming backgrounds. Our approach differs from most machine learning for undergraduate courses, which usually teach about machine learning on a language-centric code-first basis. However, while not everyone needs to deploy a model in production, we should open the black box so they understand what they consume and help build through their data and feedback. Our results will contribute to the growing field of machine learning education as we explore students conceptual understanding about Machine Learning and its algorithms. Machine learning is relatively ubiquitous in many different fields and contexts. There are new developments that include machine learning algorithms that students interact with every day. From the auto-complete feature in their cellphones to the content they are shown while browsing the internet, machine learning algorithms shape the way students experience the world. Therefore, students must critically understand this technology and its implications for their lives. This study focuses on a four-week course module that included learning activities designed for students to develop their intuition regarding the benefits of implementing machine learning before moving to a programming language. Students recognize commercial uses of machine learning algorithms, apply the concepts in a visual programming environment, and reflect on the benefits and limitations of the algorithms. This work-in-progress paper presents the work of 13 participants who completed at least one of the following learning activities. The participants completed reflection activities that represented retrieval practice opportunities to consolidate their learning and a final project to demonstrate their understanding of machine learning and how it may be implemented. We analyzed the qualitative data of student work to describe how the suggested progression supports learning about machine learning.