When to use different machine learning algorithms: a simple guide

If you've been at machine learning long enough, you know that there is a "no free lunch" principle -- there's no one-size-fits-all algorithm that will help you solve every problem and tackle every dataset. I work for Springboard -- we've put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee. What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model.