Classification is one of the core workloads in machine learning and a natural starting point for budding data scientists. Humans have a natural inclination to classify things--that cloud looks like a tree!--but when we apply this to computers, we need to apply a bit of rigor. This talk supplies that bit of rigor, covering the necessary background to solving a classification problem. We will define key terminology, review some of the most popular and effective classification algorithms available today, and explain the aptly-named confusion matrix along the way. Examples will be primarily in Python, although no prior knowledge of the language will be necessary for this session.