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)

To get the most out of this book

You will need a version of Python installed on your computer—the latest version, if possible. All code examples have been tested using Python 3.10 on Windows. However, they should work with future version releases too.

Software/hardware covered in the book

Operating system requirements

Python 3.10

Windows, macOS, or Linux

Microsoft C++ Build Tools


The Python examples in the book are available as Jupyter notebooks, and you need to use an IDE such as Visual Studio Code ( to run them. You also need a Gmail account to download specific resources.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

In certain notebooks, the code uses reduced versions of the datasets to limit the run time to an acceptable level. Feel free to adjust the size of the datasets based on your system configuration. At the end of each chapter, you are strongly urged to re-execute the code by alternating the configuration of each machine learning algorithm.