Book Image

Machine Learning Techniques for Text

By : Nikos Tsourakis
Book Image

Machine Learning Techniques for Text

By: Nikos Tsourakis

Overview of this book

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.
Table of Contents (13 chapters)

Introducing deep neural networks

Nature has always inspired mathematicians and engineers to devise appropriate algorithms, designs, and artifacts for any given problem. So incorporating solutions that have proven themselves over millennia seems like a good idea. We can refer to numerous examples such as bats’ echolocation that inspired human-made sonars, the high-speed trains that have a shape that resembles the elongated beak of kingfisher birds to prevent sonic booms, the flight of drones as a flock of birds to avoid collisions, and many more.

Throughout this chapter, we discussed many times how algorithms learn from data. What is more natural than to think that emulating the human brain and its functionalities can enhance artificial cognition? Exploiting the mode of operation of this astonishingly complex organ of the human nervous system might permit the creation of sophisticated algorithms in any domain. This section provides a gentle introduction to the topic and presents...