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 a bit closer to implementing a machine learning solution with Qlik tools. We discovered the importance of forming a correct business question to be solved and what aspects should be considered when forming a problem.

We discovered some general steps of data preparation and how these should be handled. More detailed techniques for data modeling and transformations are introduced in Chapter 6. We also investigated the concept of model preparation and validation briefly.

At the end of this chapter, we discussed the importance of presentation and what to consider when visualizing results with Qlik. This topic is considered in more detail in Chapter 9.

In the next chapter, we are going to find out how to prepare environments for our machine learning solutions. We will install on-premises integration for R and Python, discover how to utilize APIs and Advanced Analytics integration to interact with Azure ML Studio and AWS SageMaker, and...