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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

Python is a powerful language that will serve you well, regardless of your area of expertise. In this chapter, we discussed some of the most important concepts when working with the command line, such as creating directories, installing packages, and creating and editing Python scripts. We also discussed the Python programming language quite extensively. We reviewed some of the most commonly used IDEs, general data types, and calculations. We also reviewed some of the more complex data types such as lists, DataFrames, and JSON files. We also looked over the basics of APIs and making HTTP requests, and we introduced OOP with regard to Python classes. All of the examples we explored in this chapter relate to applications commonly discussed within the field of data science, so having a strong understanding of them will be very beneficial.

Although this chapter was designed to introduce you to some of the most important concepts in data science (such as variables, lists, JSON...

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