Machine learning practitioners are often of the opinion that creating models is easy, but creating a good one is much more difficult. Indeed, not only is creating a good model important, but perhaps more importantly, knowing how to identify a good model is what distinguishes successful versus less successful Machine Learning endeavors.

In this chapter, we read up on some of the deeper theoretical concepts in Machine Learning. Bias, Variance, Regularization, and other common concepts were explained with examples as and where needed. With accompanying R code, we also learnt about some of the common machine learning algorithms such as Random Forest, Support Vector Machines, and others. We concluded with a tutorial on how to create an exhaustive web-based application for Association Rules Mining against CMS OpenPayments data.

In the next chapter, we will read about some of the technologies that are being used in enterprises for both big data as well as machine learning. We will also discuss the merits of cloud computing and how they are influencing the selection of enterprise software and hardware stacks.