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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

Features of Qlik AutoML

Qlik AutoML is a tool within the Qlik Sense analytics platform that automates the process of building and deploying machine learning models. It simplifies the machine learning workflow and allows users to create predictive models, without requiring in-depth knowledge of data science or programming. Some of the key features of Qlik AutoML include the following:

  • Automated model selection: Qlik AutoML automatically selects the best machine learning algorithm based on data and the prediction task, saving users from manually exploring and comparing different algorithms.
  • Hyperparameter tuning: Qlik AutoML optimizes the hyperparameters of the selected machine learning model to improve its performance and accuracy. Hyperparameter tuning helps fine-tune the model’s behavior and makes it more effective in making predictions.
  • Cross-validation: Qlik AutoML uses cross-validation techniques to evaluate the performance of models. It splits data into...
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