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)


In this introductory chapter, we provided a high-level description of the themes covered in the book. First, we discussed different aspects of human language and what makes it such a unique resource. On the other hand, it can pose many challenges when processing human text, with ambiguity being the most serious threat.

Then, the discussion went into the current data explosion identifying the defining properties of big data. For AI, we presented its main types and the driving forces that led to its take-off. We also introduced the cutting-edge topics of ML, DL, and NLP. In this context, we set our own playground at the intersection of these fields.

A large part of the chapter was dedicated to the new paradigm shift in software programming imposed by ML. We also discussed the basic taxonomy of this emerging field. Finally, we concluded with the visualization and evaluation topics encountered many times throughout the book.

The next chapter deals with the first case study...