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 within the Qlik platform
Apply your knowledge of ML to real-world scenarios with the help of practical examples
Purchase of the print or Kindle book includes a free PDF eBook
Book Description
The ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions.
You’ll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset.
By the end of this book, you’ll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.
What you will learn
Find out how to build practical machine learning solutions with the Qlik platform
Develop the skills needed to generate and verify machine learning models using the Qlik platform
Discover techniques used for preparing and investigating data to craft machine learning solutions
Understand how to transform real-world business problems into machine learning models
Expand your potential to new use cases with data analytics
Explore advanced visualization techniques to make your models come alive
Who this book is for
If you’re interested in data and analytics and are looking to extend your skillset to machine learning, this book is for you. Basic working knowledge of data, preferably with Qlik tools, will help you get started with this book. This is an excellent guide for anyone who wants to start using machine learning as part of their data analytics journey.
Author(s): Hannu Ranta
Publisher: Packt
Year: 2023
Language: English
Pages: 242
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
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