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Machine Learning in Biotechnology and Life Sciences

You're reading from   Machine Learning in Biotechnology and Life Sciences Build machine learning models using Python and deploy them on the cloud

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801811910
Length 408 pages
Edition 1st Edition
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Author (1):
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Saleh Alkhalifa Saleh Alkhalifa
Author Profile Icon Saleh Alkhalifa
Saleh Alkhalifa
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Data
2. Chapter 1: Introducing Machine Learning for Biotechnology FREE CHAPTER 3. Chapter 2: Introducing Python and the Command Line 4. Chapter 3: Getting Started with SQL and Relational Databases 5. Chapter 4: Visualizing Data with Python 6. Section 2: Developing and Training Models
7. Chapter 5: Understanding Machine Learning 8. Chapter 6: Unsupervised Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Understanding Deep Learning 11. Chapter 9: Natural Language Processing 12. Chapter 10: Exploring Time Series Analysis 13. Section 3: Deploying Models to Users
14. Chapter 11: Deploying Models with Flask Applications 15. Chapter 12: Deploying Applications to the Cloud 16. Other Books You May Enjoy

Chapter 9: Natural Language Processing

In the previous chapter, we discussed using deep learning to not only address structured data in the form of tables but also sequence-based data where the order of the elements matters. In this chapter, we will be discussing another form of sequence-based data – text, within a field known as Natural Language Processing (NLP). We can define NLP as a subset of artificial intelligence that overlaps with both the realms of machine learning and deep learning, specifically when it comes to interactions between the areas of linguistics and computer science.

There are many well-known and well-documented applications and success stories of using NLP for various tasks. Products ranging from spam detectors all the way to document analyzers involve NLP to some extent. Throughout this chapter, we will explore several different areas and applications involving NLP.

As we have observed with many other areas of data science we have explored thus...

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