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)
1
Part 1:Concepts of Machine Learning
6
Part 2: Machine learning algorithms and models with Qlik
12
Part 3: Case studies and best practices

Summary

In this chapter, we started to move toward the practical implementation of machine learning models with Qlik. To prepare for the coming chapters, we installed different environments.

First, we went through the installation of the R environment and the Advanced Analytics connection from a client-managed Qlik Sense instance in R. We covered the steps to run the components as a service and created a simple sample application to verify the functionality.

We also installed a Python environment and connected that to our Qlik environment using Advanced Analytics Integration. We demonstrated the functionality of this environment using an example application. At the end of this chapter, we moved from on-premises into cloud environments and discovered how to create connections to external AI and machine learning platforms using REST. We also did a few setup steps for our AutoML environment to get it ready for our coming chapters.

In the next chapter, we will investigate the...