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

Neural networks, deep learning, and natural-language models

Neural networks are a type of machine-learning algorithm that is inspired by the structure and function of the human brain. They are composed of layers of interconnected nodes or artificial neurons that process and transmit information.

In a neural network, the input data is fed into the first layer of nodes, which applies a set of mathematical transformations to the data and produces an output. The output of the first layer is then fed into the second layer, which applies another set of transformations to produce another output, and so on until the final output is produced.

The connections between the nodes in the neural network have weights that are adjusted during the learning process to optimize the network’s ability to make accurate predictions or classifications. This is typically achieved using an optimization algorithm such as stochastic gradient descent. An example of the structure of a neural network...