Machine learning is a field of statistics that enables machines to learn with data, which is often used to automate tasks and make predictions. Lauren Shin, a developer relations intern at Neo4j, presented on machine learning and its applications in graph databases. She introduced three approaches to better data analysis through machine learning: Approach 1 uses user-defined procedures in Cypher to train models from the Neo4j browser while keeping data within the graph; Approach 2 eliminates data export by using graph databases to store and hold data during model development; and Approach 3 restricts comparisons with clustering, where graph structures are used to infer groups of similar users. Shin also discussed model version control, which represents relationships between different versions of models in a graph, increasing transparency in the process of building machine learning models. Additionally, she highlighted the potential of new graph-based machine learning algorithms that rely on graph-based input instead of vectors, offering insights and benefits not available with traditional vector-based methods.