In this chapter, we first got an introduction of Qlik tools for machine learning. We discovered the key features of the platform and how different components can be utilized. Understanding the key components is important since we will be utilizing Insight Advisor, AutoML, and Advanced Analytics Integration later in this book.
We also discovered some of the key concepts of statistics. Understanding the basics of the underlying mathematics is crucial to understanding the models. We only scratched the surface of the mathematics, but this should be enough to familiarize you with the terminology. We also touched on the important topic of sample and sample size. When creating a model, we need to train it with training data. Determining a reasonable sample size will help us to get an accurate model without wasting resources.
At the end of this chapter, we got familiar with some of the techniques to validate the model’s performance and reliability. These are important concepts, since Qlik tools are using the introduced methods to communicate the metrics of the model.
In the next chapter, we will augment our background knowledge by getting familiar with some of the most common machine-learning algorithms. These algorithms will be used in later parts of this book.