Book Image

Machine Learning with Qlik Sense

By : Hannu Ranta
Book Image

Machine Learning with Qlik Sense

By: Hannu Ranta

Overview of this book

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.
Table of Contents (17 chapters)
Part 1:Concepts of Machine Learning
Part 2: Machine learning algorithms and models with Qlik
Part 3: Case studies and best practices


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


accuracy 22

example scenario 22

AdaBoost (Adaptive Boosting) 49

adjusted R-squared 20

Advanced Analytics connection

live connection 132

load time connection 132

used, for building model in on-premises environment 132-140

Advanced Analytics Integration 7, 81

installing, with Python 90-93

installing, with R 82-90

workflow 82

Amazon SageMaker connector 96

parameters 96

area under the curve (AUC) 25

artificial intelligence (AI) 205

future trends 205-207


AUC-ROC 22, 25

Azure ML connector 96

parameters 96, 97


bar charts 173

Bayes’ theorem 11

binary classification 69

scoring 21

boosting 49

boosting algorithms

AdaBoost (Adaptive Boosting) 49

Gradient Boosting 49

XGBoost 49

box plots...