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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

Introduction to NLP

Within the scope of biotechnology, we often turn to NLP for numerous reasons, which generally involve the need to organize data and develop models to find answers to scientific questions. As opposed to the many other areas we have investigated so far, NLP is unique in the sense that we focus on one type of data at hand: text data. When we think of text data within the realm of NLP, we can divide things into two general categories: structured data and unstructured data. We can think of structured data as text fields living within tables and databases in which items are organized, labeled, and linked together for easier retrieval, such as a SQL or DynamoDB database. On the other hand, we have what is known as unstructured data such as documents, PDFs, and images, which can contain static content that is neither searchable nor easily accessible. An example of this can be seen in the following diagram:

Figure 9.1 – Structured and unstructured...

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