In this chapter, we covered some statistical concepts before getting started with predictive analytics. Some examples are random sampling, hypothesis testing, the chi-square test, correlation, expectation, variance, covariance and Bayes' rule, and so on. In the second part of this chapter, we discussed probability and information theory for predictive analytics. The central objects of probability theory are random variables, stochastic processes, and events, which are also discussed in this chapter.
We have provided some theoretical aspects. However, predictive models are models of the relation between the specific performance of a unit in a sample and one or more known attributes and features of the unit. The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance.
The next chapter describes the main TensorFlow capabilities, motivated by a real-life Titanic example. The second part of the chapter will cover...