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)

Relevant research fields

Parallel to AI, another field has continuously gained traction over the past decades. ML is how a computer system develops its intelligence, used by AI to carry out its tasks. Their relation is shown in Figure 1.1:

Figure 1.1 – How AI, ML, DL, and NLP are related

ML is a subset of AI and its intelligence is encompassed by a model trained over several iterations on a large amount of data. With minimal human intervention, the ML algorithm tries to identify patterns from past experiences and develop an efficient model to make predictions. As the ML algorithm is exposed to more data over time, its performance improves.

Interesting fact

The term machine learning was coined in 1959 by Arthur Samuel as the field of study that allows computers to learn without being explicitly programmed.

One way to perform training is to use a special kind of architecture stemming from deep learning (DL). DL algorithms mimic the human brain...