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

Summary

In this chapter, we first got an introduction of Qlik tools for machine learning. We discovered the key features of the platform and how different components can be utilized. Understanding the key components is important since we will be utilizing Insight Advisor, AutoML, and Advanced Analytics Integration later in this book.

We also discovered some of the key concepts of statistics. Understanding the basics of the underlying mathematics is crucial to understanding the models. We only scratched the surface of the mathematics, but this should be enough to familiarize you with the terminology. We also touched on the important topic of sample and sample size. When creating a model, we need to train it with training data. Determining a reasonable sample size will help us to get an accurate model without wasting resources.

At the end of this chapter, we got familiar with some of the techniques to validate the model’s performance and reliability. These are important concepts, since Qlik tools are using the introduced methods to communicate the metrics of the model.

In the next chapter, we will augment our background knowledge by getting familiar with some of the most common machine-learning algorithms. These algorithms will be used in later parts of this book.