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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

Creating a Good Machine Learning Solution with the Qlik Platform

This chapter provides an overview of the key steps involved in creating effective machine learning solutions with Qlik. The same principles apply to all machine learning tools and solutions.

The process starts with defining the problem to be solved and aligning it with the organization’s goals. Data preparation is critical and Qlik provides robust data preparation capabilities for collecting, cleaning, and transforming data to ensure its quality and relevance.

Data exploration and visualization using Qlik’s features are essential for understanding data patterns and informing the machine learning model. Model selection is crucial and Qlik offers a wide range of algorithms for different use cases.

Once the model is selected, it needs to be trained using the corresponding tools. Model evaluation using appropriate metrics helps assess its performance and make necessary refinements.

Deployment of...

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