In this chapter, we learnt about the basic fundamentals of Machine Learning, the different types such as Supervised and Unsupervised and major concepts such as data pre-processing, data imputation, managing imbalanced classes and other topics.

We also learnt about the key distinctions between terms that are being used interchangeably today, in particular the terms AI and Machine Learning. We learned that artificial intelligence deals with a vast array of topics, such as game theory, sociology, constrained optimizations, and machine learning; AI is much broader in scope relative to machine learning.

Machine learning facilitates AI; namely, machine learning algorithms are used to create systems that are artificially intelligent, but they differ in scope. A regression problem (finding the line of best fit given a set of points) can be considered a machine learning algorithm, but it is much less likely to be seen as an AI algorithm (conceptually, although it technically could be).

In the next chapter, we will look at some of the other concepts in Machine Learning such as Bias, Variance and Regularization. We will also read about a few important algorithms and learn how to apply them using machine learning packages in R.