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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

Summary

SQL is a powerful language when it comes to querying vast amounts of data from relational databases—a skill that will serve you well in all areas of technology and most areas of biotechnology. As most companies begin to grow their database capabilities, you will likely encounter databases of many kinds, especially relational databases.

When it comes to theory, we discussed some of the most important characteristics of relational databases and how data is generally normalized. We looked over an example of patient data and how a table could be normalized to reduce repetition when being stored. We also looked over some of the most common open source and enterprise databases available and readily used on the market today.

When it comes to applications, we put together a robust AWS RDS database server and deployed it to the cloud. We then connected our local instance of MySQL to that server and populated it with a new database using a CSV file. We then went over some...

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