The predictive stack for analytics is an extremely wide and varied domain. Many ambiguous buzz words and disciplines can be associated with this field. Statistical modeling, machine learning, artificial intelligence, neural networks, deep learning, cognitive computing, and the list goes on. The variety of definitions available for each of these disciplines makes it difficult to articulate the similarities and differences between them. Our initial exercises were aligned towards statistical modeling; we will now focus more on machine learning. The difference between the two is mainly the school that they originate from. Statistical modeling comes from the mathematical school whereas machine learning evolved from computer science.
In this chapter, we'll enhance our predictive analytics skills using cutting-edge machine learning algorithms that will help us predict with better accuracy. From the time we started solving the...