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Machine Learning with Qlik Sense

You're reading from   Machine Learning with Qlik Sense Utilize different machine learning models in practical use cases by leveraging Qlik Sense

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781805126157
Length 242 pages
Edition 1st Edition
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Author (1):
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Hannu Ranta Hannu Ranta
Author Profile Icon Hannu Ranta
Hannu Ranta
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Table of Contents (17) Chapters Close

Preface 1. Part 1:Concepts of Machine Learning
2. Chapter 1: Introduction to Machine Learning with Qlik FREE CHAPTER 3. Chapter 2: Machine Learning Algorithms and Models with Qlik 4. Chapter 3: Data Literacy in a Machine Learning Context 5. Chapter 4: Creating a Good Machine Learning Solution with the Qlik Platform 6. Part 2: Machine learning algorithms and models with Qlik
7. Chapter 5: Setting Up the Environments 8. Chapter 6: Preprocessing and Exploring Data with Qlik Sense 9. Chapter 7: Deploying and Monitoring Machine Learning Models 10. Chapter 8: Utilizing Qlik AutoML 11. Chapter 9: Advanced Data Visualization Techniques for Machine Learning Solutions 12. Part 3: Case studies and best practices
13. Chapter 10: Examples and Case Studies 14. Chapter 11: Future Direction 15. Index 16. Other Books You May Enjoy

Exploring data and finding insights

Qlik Insight Advisor is a feature in Qlik Sense that uses natural language processing and artificial intelligence to help users explore data and discover insights. By using Qlik Insight Advisor, you can leverage the power of natural language processing and AI to explore your data, ask questions, and discover meaningful insights.

In Chapter 1, we already learned some of the basics of Insight Advisor. It is also possible to explore data and find insights using Qlik objects and visualizations without the augmented intelligence features. We will take a closer look at how to create visualizations this way in a later section of this book. In this section, we will discover how Insight Advisor will help us to get meaningful insights out of the data.

We will begin by using the Sales Multi Table.xlsx file as our source data. We already created a data model on top of that dataset earlier in this chapter and loaded the data into an associative engine....

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