Machine Learning with Qlik Sense : Utilize Different Machine Learning Models in Practical Use Cases by Leveraging Qlik Sense

Machine Learning with Qlik Sense Utilize Different Machine Learning Models in Practical Use Cases by Leveraging Qlik Sense

Master the art of machine learning by using the one-of-a-kind Qlik platform, and take your data analytics skills to the next level Key Features Gain a solid understanding of machine learning concepts and learn to effectively define a problem Explore the application of machine learning principles wit...

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Detalles Bibliográficos
Otros Autores: Ranta, Hannu, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2023]
Edición:First edition
Materias:
Management information systems.
Business intelligence > Computer programs.
Machine learning.
Information visualization.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009781239506719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Dedication
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Concepts of Machine Learning
  • Chapter 1: Introduction to Machine Learning with Qlik
  • Introduction to Qlik tools
  • Insight Advisor
  • Qlik AutoML
  • Advanced Analytics Integration
  • Basic statistical concepts with Qlik solutions
  • Types of data
  • Mean, median, and mode
  • Variance
  • Standard deviation
  • Standardization
  • Correlation
  • Probability
  • Defining a proper sample size and population
  • Defining a sample size
  • Training and test data in machine learning
  • Concepts to analyze model performance and reliability
  • Regression model scoring
  • Multiclass classification scoring and binary classification scoring
  • Feature importance
  • Summary
  • Chapter 2: Machine Learning Algorithms and Models with Qlik
  • Regression models
  • Linear regression
  • Logistic regression
  • Lasso regression
  • Clustering algorithms, decision trees, and random forests
  • K-means clustering
  • ID3 decision tree
  • Boosting algorithms and Naive Bayes
  • XGBoost
  • Gaussian Naive Bayes
  • Neural networks, deep learning, and natural-language models
  • Summary
  • Chapter 3: Data Literacy in a Machine Learning Context
  • What is data literacy?
  • Critical thinking
  • Research and domain knowledge
  • Communication
  • Technical skills
  • Informed decision-making
  • Data strategy
  • Summary
  • Chapter 4: Creating a Good Machine Learning Solution with the Qlik Platform
  • Defining a machine learning problem
  • Cleaning and preparing data
  • Example 1 - one-hot encoding
  • Example 2 - feature scaling
  • Preparing and validating a model
  • Visualizing the end results
  • Summary
  • Part 2: Machine learning algorithms and models with Qlik
  • Chapter 5: Setting Up the Environments
  • Advanced Analytics Integration with R and Python.
  • Installing Advanced Analytics Integration with R
  • Installing Advanced Analytics Integration with Python
  • Setting up Qlik AutoML
  • Cloud integrations with REST
  • General Advanced Analytics connector
  • Amazon SageMaker connector
  • Azure ML connector
  • Qlik AutoML connector
  • Summary
  • Chapter 6: Preprocessing and Exploring Data with Qlik Sense
  • Creating a data model with the data manager
  • Introduction to the data manager
  • Introduction to Qlik script
  • Important functions in Qlik script
  • Validating data
  • Data lineage and data catalogs
  • Data lineage
  • Data catalogs
  • Exploring data and finding insights
  • Summary
  • Chapter 7: Deploying and Monitoring Machine Learning Models
  • Building a model in an on-premises environment using the Advanced Analytics connection
  • Monitoring and debugging models
  • Summary
  • Chapter 8: Utilizing Qlik AutoML
  • Features of Qlik AutoML
  • Using Qlik AutoML in a cloud environment
  • Creating and monitoring a machine learning model with Qlik AutoML
  • Connecting Qlik AutoML to an on-premises environment
  • Best practices with Qlik AutoML
  • Summary
  • Chapter 9: Advanced Data Visualization Techniques for Machine Learning Solutions
  • Visualizing machine learning data
  • Chart and visualization types in Qlik
  • Bar charts
  • Box plots
  • Bullet charts
  • Distribution plots
  • Histogram
  • Maps
  • Scatter plots
  • Waterfall charts
  • Choosing visualization type
  • Summary
  • Part 3: Case studies and best practices
  • Chapter 10: Examples and Case Studies
  • Linear regression example
  • Customer churn example
  • Summary
  • Chapter 11: Future Direction
  • The future trends of machine learning and AI
  • How to recognize potential megatrends
  • Summary
  • Index
  • About Packt
  • Other Books You May Enjoy.

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