Practical Big Data Analytics

Summary

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 ...