The machine learning models that we have discussed so far in the previous two chapters share one common characteristic: they require training data containing ground truth. This implies a dataset containing true values of the predicate or dependent variable that is often manually labeled. Such machine learning where the algorithm is trained using labeled data is called supervised learning. This type of machine learning gives a very good performance in terms of accuracy of prediction. It is, in fact, the de facto method used in most industrial systems using machine learning. However, the drawback of this method is that, when one wants to train a model with large datasets, it would be difficult to get the labeled data. This is particularly relevant in the era of Big Data as a lot of data is available for organizations from various logs, transactions, and interactions with consumers; organizations want to gain insight from this data and make...