Summary

In this chapter, we have gained a high-level view of various basic building blocks and subcomponents involved in statistical modeling and machine learning, such as mean, variance, interquartile range, p-value, bias versus variance trade-off, AIC, Gini, area under the curve, and so on with respect to the statistics context, and cross-validation, gradient descent, and grid search concepts with respect to machine learning. We have explained all the concepts with the support of both Python and R code with various libraries such as numpy, scipy, pandas, and scikit- learn, and the stats model in Python and the basic stats package in R. In the next chapter, we will learn to draw parallels between statistical models and machine learning models with linear regression problems and ridge/lasso regression in machine learning using both Python and R code.