Practical Machine Learning

Summary

In this chapter, we have explored two learning algorithms, instance-based and kernel methods, and we have seen how they address the classification and prediction requirements. In the instance-based learning methods, we explored the Nearest Neighbor algorithm in detail and have seen how to implement this using our technology stack, Mahout, Spark, R, Julia, and Python. Similarly, in the kernel-based methods, we have explored SVM. In the next chapter, we will cover the Association Rule-based learning methods with a focus on Apriori and FP-growth algorithms.