Summary

In this chapter, you have learned Bayesian Machine learning and how to implement Naïve Bayes classifiers association rule-based learning with Mahout, R, Python, Julia, and Spark. Additionally, we covered all the core concepts of statistics, starting from basic nomenclature to various distributions. We have covered the Bayes' theorem in depth with examples to understand how to apply it to the real-world problems.

In the next chapter, we will be covering the regression-based learning techniques and in specific, the implementation for linear and logistic regression.