In recent years, the world of artificial intelligence has been dominated by Generative Artificial Intelligence (GenAI). However, traditional models like linear regression and decision trees remain the powerhouse behind machine learning (ML) applications dealing with structured data such as tables. Borna demonstrates APLearn, an APL library implementing common ML algorithms for a variety of tasks such as supervised learning, clustering, and dimensionality reduction. He explores array programming’s relevance in the context of ML, its unique advantages and occasional drawbacks, and trains a couple of models on real data to get a taste for what ML in APL might look like in practice.