Ekaterina Kochmar

Ekaterina Kochmar is a lecturer (assistant professor) at the Department of Computer Science of the University of Bath, where she is part of the AI research group. Her research lies at the intersection of artificial intelligence, natural language processing, and intelligent tutoring systems. She holds a PhD in natural language processing, an MPhil in advanced computer science from the University of Cambridge, and an MA in computational linguistics from the University of Tuebingen. She is also a cofounder and the chief scientific officer of Korbit AI, focusing on building an AI-powered dialogue-based tutoring system capable of providing learners with high-quality, interactive, and personalized education. Ekaterina has extensive experience in teaching both within and outside of academia.

books by Ekaterina Kochmar

Natural Language Processing in Practice
  • December 2020
  • ISBN 9781617299360
  • 88 pages
Included with a Manning Online subscription

Natural Language Processing in Practice showcases two chapters from Manning’s Getting Started with Natural Language Processing. In it, you’ll learn fundamental NLP skills and concepts as author and NLP expert Ekaterina Kochmar guides you step by step through building two complete NLP applications—both illustrating classic examples of NLP in action. Jumpstart your NLP learning with this free practical mini ebook!

Getting Started with Natural Language Processing
  • September 2022
  • ISBN 9781617296765
  • 456 pages
Included with a Manning Online subscription
  • printed in black & white
catalog / Data Science / Machine Learning / Natural Language Processing

Getting Started with Natural Language Processing teaches you how to upgrade user-facing applications with text and speech-based features. From the accessible explanations and hands-on examples in this book you’ll learn how to apply NLP to sentiment analysis, user profiling, and much more. As you go, each new project builds on what you’ve previously learned, introducing new concepts and skills. Handy diagrams and intuitive Python code samples make it easy to get started—even if you have no background in machine learning!