Intro to Classification and Feature Selection with XGBoost

I recently came across a new [to me] approach, gradient boosting machines (specifically XGBoost), in the book Deep Learning with Python by François Chollet. Chollet mentions that XGBoost is the one shallow learning technique that a successful applied machine learner should be familiar with today, so I took his word for it and dove in to learn more. I mostly wanted to write this article because I thought that others with some knowledge of machine learning also may have missed this topic as I did. I am by no means an expert on the topic and to be honest had trouble understanding some of the mechanics, however, I hope this article is a great primer to your exploration on the subject (list of great resources at the bottom too)! So what is XGBoost and where does it fit in the world of ML? Gradient Boosting Machines fit into a category of ML called Ensemble Learning, which is a branch of ML methods that train and predict with many models at once to produce a single superior output.