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)

Visualization of the data

The vast majority of all human communication is visual. The reason is that we are wired to understand images instantly while we need to process text. For instance, visual artifacts such as maps have been around for centuries to help understand data, so it is not surprising that most people are visual learners and can easily retain the information they see. In addition, visuals make it much easier to spot patterns and identify anomalies, which is critical to people working with data. Technology ignited the need for better data visualizations to represent and present data.

A good visualization should encompass three characteristics: being trustworthy, accessible, and elegant. By saying it is trustworthy, we refer to the fact that the data is honestly portrayed. For example, if the visual suggests a relationship, trend, or correlation, the data should support that relationship; otherwise, we are just deceiving the audience. An accessible visualization refers...