How to choose the right algorithm for your machine learning problem

With the recent machine learning boom, more and more algorithms have become available that perform exceptionally well on a number of tasks. But knowing beforehand which algorithm will perform best on your specific problem is often not possible. If you had infinite time at your disposal, you could just go through all of them and try them out. The following post shows you a better way to do this, step by step, by relying on known techniques from model selection and hyper-parameter tuning. Before we get in too deep, we want to make sure we brushed up on the basics. In specific, we should know that there are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.