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

Features of Qlik AutoML

Qlik AutoML is a tool within the Qlik Sense analytics platform that automates the process of building and deploying machine learning models. It simplifies the machine learning workflow and allows users to create predictive models, without requiring in-depth knowledge of data science or programming. Some of the key features of Qlik AutoML include the following:

  • Automated model selection: Qlik AutoML automatically selects the best machine learning algorithm based on data and the prediction task, saving users from manually exploring and comparing different algorithms.
  • Hyperparameter tuning: Qlik AutoML optimizes the hyperparameters of the selected machine learning model to improve its performance and accuracy. Hyperparameter tuning helps fine-tune the model’s behavior and makes it more effective in making predictions.
  • Cross-validation: Qlik AutoML uses cross-validation techniques to evaluate the performance of models. It splits data into...