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Everything You Need
to Get up and running
with Getting Started with Natural Language
Processing.
Take 42% off the book by entering code
slkochmar into the discount code box at
checkout at manning.com.
Natural Language Processing is a set of data science techniques that
enable machines to make sense of human text and speech. Advances
in machine learning and deep learning have made NLP more efficient
and reliable than ever, leading to a huge number of new tools and
resources.
Get started!
Getting Started with Natural Language Processing gives you
everything you need to get started with NLP in a friendly,
understandable tutorial. Full of Python code and hands-on
projects, each chapter provides a concrete example with
practical techniques that you can put into practice right away.
If you’re a beginner to NLP and want to upgrade your applications
with functions and features like information extraction, user profiling,
and automatic topic labeling, this is the book for you.
When you’re done, you’ll have a solid grounding in NLP that will serve
as a foundation for further learning.
What people are saying
about the book:
Great
introduction
to NLP!
-Jonathan Wood
Great content, tone,
presentation, figures,
and code. Pedagogical
and thorough—friendly
and engaging.
-Erik Hansson
Extremely helpful!
Every important detail
in engineering and
deploying algorithms
covered in depth.
-Luis Fernando Fontoura de
Oliveira
About the author:
Ekaterina Kochmar is an Affiliated Lecturer and a Senior Research
Associate at the Natural Language and Information Processing
group of the Department of Computer Science and Technology,
University of Cambridge. She holds an MA degree in
Computational Linguistics, an MPhil in Advanced Computer
Science, and a PhD in Natural Language Processing.
Take 42% off Getting Started with Natural
Language Processing by entering code
slkochmar into the discount code box at
checkout at manning.com.
Check out the contents of the book on our
browser-based reader liveBook here.

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The Guide to Starting with NLP

  • 1. Everything You Need to Get up and running with Getting Started with Natural Language Processing. Take 42% off the book by entering code slkochmar into the discount code box at checkout at manning.com.
  • 2. Natural Language Processing is a set of data science techniques that enable machines to make sense of human text and speech. Advances in machine learning and deep learning have made NLP more efficient and reliable than ever, leading to a huge number of new tools and resources.
  • 3. Get started! Getting Started with Natural Language Processing gives you everything you need to get started with NLP in a friendly, understandable tutorial. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away.
  • 4. If you’re a beginner to NLP and want to upgrade your applications with functions and features like information extraction, user profiling, and automatic topic labeling, this is the book for you. When you’re done, you’ll have a solid grounding in NLP that will serve as a foundation for further learning.
  • 5. What people are saying about the book: Great introduction to NLP! -Jonathan Wood Great content, tone, presentation, figures, and code. Pedagogical and thorough—friendly and engaging. -Erik Hansson Extremely helpful! Every important detail in engineering and deploying algorithms covered in depth. -Luis Fernando Fontoura de Oliveira
  • 6. About the author: Ekaterina Kochmar is an Affiliated Lecturer and a Senior Research Associate at the Natural Language and Information Processing group of the Department of Computer Science and Technology, University of Cambridge. She holds an MA degree in Computational Linguistics, an MPhil in Advanced Computer Science, and a PhD in Natural Language Processing.
  • 7. Take 42% off Getting Started with Natural Language Processing by entering code slkochmar into the discount code box at checkout at manning.com. Check out the contents of the book on our browser-based reader liveBook here.