Summary

In this chapter, we have gained an overview of different machine-learning algorithms. We have discovered how different algorithms can be used to solve problems and how they function. We started this chapter by getting familiar with some of the most common regression algorithms and gained knowledge on how to use these in R and Python. We discovered how to utilize clustering, decision trees, and random forests with practical examples.

In the later part of this chapter, we moved on to more complex algorithms and learned how different boosting algorithms, neural networks, and other advanced models function. These models are utilized in Qlik AutoML, and it’s important to know how each model is structured. After reading this chapter, you now have a basic understanding of the models and are prepared to utilize these with Qlik tools. We will use most of these algorithms in the later parts of this book.

In the next chapter, we will focus on data literacy in a machine-learning...