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

Using Qlik AutoML in a cloud environment

There are several steps when deploying a machine learning model using Qlik AutoML. These steps are illustrated in the following diagram:

Figure 8.1: The AutoML workflow

Figure 8.1: The AutoML workflow

As you might remember from our earlier chapters, the first step of every machine learning project is to define a business problem and question, followed by the steps required for data cleaning, preparation, and modeling. Typically, data cleaning and transformation part can take up 80–90% of the time spent on a project.

Once we have a machine-learning-ready dataset, we will continue by creating a machine learning experiment.

In automated machine learning, the process of training machine learning algorithms on a specific dataset and target is automated. When you create an experiment and load your dataset, the system automatically examines and prepares data for machine learning. It provides you with statistics and insights about each column...