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)
1
Part 1:Concepts of Machine Learning
6
Part 2: Machine learning algorithms and models with Qlik
12
Part 3: Case studies and best practices

Visualizing machine learning data

When visualizing machine learning data, several important aspects should be considered to ensure effective communication and interpretation of the insights. The following should be considered (these are not tool-specific):

  • Understand the data: Gain a deep understanding of the data you are working with. Analyze its structure, distributions, and relationships to identify key variables and patterns. This understanding will guide you in selecting appropriate visualization techniques.
  • Choose the right visualization techniques: Select visualization techniques that best represent the characteristics and relationships within the data. Common techniques include scatter plots, line charts, bar charts, histograms, heatmaps, and network graphs. Choose techniques that effectively convey the information you want to communicate. In the next chapter, we will discover the different visualizations that Qlik offers.
  • Simplify and reduce complexity: Keep...