Machine Learning Techniques for Text

"Machine Learning Techniques for Text" is your essential guide to mastering modern techniques for text processing and natural language understanding using Python. Through hands-on examples and insightful case studies, you will learn key methods for text preprocessing, dimensionality reduction, classification, and evaluation, equipping you with practical skills for real-world scenarios.

What this Book will help me do

• Acquire the fundamentals of machine learning techniques for textual data.
• Learn methods for text representation, such as language modeling.
• Understand and apply text preprocessing and dimensionality reduction techniques.
• Develop machine learning models for text classification and clustering tasks.
• Evaluate and fine-tune performance metrics for text-based projects.

Author(s)

Nikos Tsourakis is an experienced data scientist and educator specializing in textual data analysis and applied machine learning. With years of professional experience in developing real-world solutions to text processing challenges, Nikos brings a pragmatic and hands-on perspective to his writing. He is skilled at distilling complex topics into actionable insights and guides readers through practical applications with real-world relevance.

Who is it for?

This book is suited for computer science professionals, data scientists, and analysts who wish to enhance their text processing skills using machine learning. It is also ideal for students in data science or language technology fields, as well as professors seeking a practical, example-driven textbook for teaching. A basic knowledge of Python is required for the hands-on exercises provided.