After completing Data Cleaning and Feature Engineering, you'll have transformed your raw dataset into an analytical base table (ABT). We call it an "ABT" because it's what you'll be building your models on. As a final tip: Not all of the features you engineer need to be winners. In fact, you'll often find that many of them don't improve your model. That's fine because one highly predictive feature makes up for 10 duds.

Welcome to our 7-part mini-course on data science and applied machine learning! Over these 7 chapters, our goal is to provide you with an end-to-end blueprint for applied machine learning, while keeping this as actionable and succinct as possible. With that, let's get started with a bird's eye view of the machine learning workflow. One really cool (optional) challenge you can do in the next hour is training your first machine learning model! That's right, we've put together a complete step-by-step tutorial for training a model that can predict wine quality.

This post will provide an overview of bagging, boosting, and stacking, arguably the most used and well-known of the basic ensemble methods. They are not, however, the only options. Random Forests is another example of an ensemble learner, which uses numerous decision trees in a single predictive model, and which is often overlooked and treated as a "regular" algorithm. There are other approaches to selecting effective algorithms as well, treated below.