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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 3: Getting Started with SQL and Relational Databases

According to a recent article published in the Journal of Big Data Analytics and Its Applications, every 60 seconds on the internet, the following happens:

  • 700,000 status updates are made.
  • 11 million messages are sent.
  • 170 million emails are received.
  • 1,820 terabytes (TB) of new data is created.

It would be an understatement to claim that data within the business landscape is growing rapidly at an unprecedented rate. With this major explosion of information, companies around the globe are investing a great deal of capital in an effort to effectively capture, analyze, and deliver benefits from this data for the company. One of the main methods by which data can be managed and subsequently retrieved to provide actionable insights is through Structured Query Language (SQL).

Similar to how we used the Terminal command line to create directories, or Python to run calculations, you can use SQL to create...

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