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

Clustering algorithms, decision trees, and random forests

Clustering algorithms are used for unsupervised learning tasks, which means they are used to find patterns in data without any predefined labels or categories. The goal of clustering algorithms is to group similar data points together in clusters, while keeping dissimilar data points separate.

There are several types of clustering algorithms, including K-means, hierarchical clustering, and density-based clustering. K-means is a popular clustering algorithm that works by dividing a dataset into K clusters, where K is a predefined number of clusters. Hierarchical clustering is another clustering algorithm that creates a hierarchy of clusters based on the similarity between data points. Density-based clustering algorithms, such as DBSCAN, group together data points that are closely packed together in high-density regions.

Decision trees, on the other hand, are used for supervised learning tasks, which means they are used...