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

What this book covers

Chapter 1, Introduction to Machine Learning with Qlik, will introduce you to the world of machine learning with the Qlik platform. This chapter covers all the basic concepts for implementing machine learning with Qlik, like R2, F1 and SHAP.

Chapter 2, Machine Learning Algorithms and Models with Qlik, will provide information about the essential algorithms and models in machine learning focusing on ones important in the Qlik platform. You will get a basic understanding of how the algorithms behind Qlik’s ML solution work and how to pick the right one for specific problems.

Chapter 3, Data Literacy in a Machine Learning Context, will cover how data literacy can be utilized in a machine learning context. You will learn and utilize data literacy skills to get the most out of the data that ML models are using.

Chapter 4, Creating a Good Machine Learning Solution with the Qlik Platform, covers the essential knowledge to create a good machine learning solution with the Qlik platform. You will learn all the steps needed to utilize automated solutions for model building.

Chapter 5, Setting Up the Environments, teaches how to set up the environments for machine learning using Qlik tools. You will get hands on examples for setting up and initializing different environments and also cover any problems that might occur during the setup, and how to fix them.

Chapter 6, Preprocessing and Exploring Data with Qlik Sense, will cover the techniques needed to preprocess the data in Qlik Sense. This chapter will guide you through all the important steps for preprocessing and exploring data. You will learn how to validate data and make data exploration efficient.

Chapter 7, Deploying and Monitoring Machine Learning Models, will cover how to deploy and monitor machine learning models in both cloud and client-managed environments. It will also cover what to consider before deploying to production.

Chapter 8, Utilizing Qlik AutoML, covers the use of Qlik AutoML tool in both cloud and on-premise environments. This chapter will guide you with the best practices and features of AutoML using real-world examples. You will also learn the features of AutoML and models that can be deployed using the tool.

Chapter 9, Advanced Data Visualization Techniques for Machine Learning Solutions, provides examples and best practices about visualizing machine learning related data with Qlik tools. This chapter covers Qlik charts and advanced features and functions to fully utilize the charts. It will also cover how to use Insight Advisor to help visualization tasks and provide insights about data.

Chapter 10, Examples and Case Studies, guides you through real world examples and use cases with Qlik’s machine learning portfolio. Each example is described in detailed level and also the information about the business value is provided.

Chapter 11, Future Direction, will give you an idea of the future development and trends of machine learning. You will get information about overall trends and how the Qlik portfolio will develop to support the adoption of new trends.