Machine Learning with Qlik Sense: Utilize different machine learning models in practical [1 ed.] 9781805126157

Table of contents :
Machine Learning with Qlik Sense
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
Part 1:Concepts of Machine Learning
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
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
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
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
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
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
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
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
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
10
Examples and Case Studies
Linear regression example
Customer churn example
Summary
11
Future Direction
The future trends of machine learning and AI
How to recognize potential megatrends
Summary
Index
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