Book Image

Machine Learning with Qlik Sense

By : Hannu Ranta
Book Image

Machine Learning with Qlik Sense

By: Hannu Ranta

Overview of this book

The ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions. You’ll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset. By the end of this book, you’ll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.
Table of Contents (17 chapters)
Part 1:Concepts of Machine Learning
Part 2: Machine learning algorithms and models with Qlik
Part 3: Case studies and best practices


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...