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 discovered the usage of Qlik AutoML. We first learned what the tool will provide for users and what its key features are. We built our first machine learning model with Qlik AutoML using the famous Iris dataset. In this section, we discovered how to run experiments and deploy a model from experimentation. We also discovered how to utilize the model in a Qlik application, both during a data load and in real time. We learned from different metrics how our model performed.

In the latter part of this chapter, we took a quick look at an on-premises environment. We learned how to utilize Qlik AutoML in hybrid scenarios and how to set up our environment in these use cases. We also discovered some of the best practices to be used with Qlik AutoML.

In the following chapter, we will dive deep into data visualization. We will discover the techniques to visualize machine-learning-related data and investigate the use of some of the lesser-used graph types. We will...