Keeping It Classy


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.


No recordings or additional media are available for this talk.