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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

Understanding time series data

When it comes to using time series data, there are endless ways to visualize and display data to effectively communicate a thought or idea. In most of the data we have used so far, we have handled features and labels in which a certain set of features generally corresponded to a label of interest. When it comes to time series data, we tend to forego the idea of a class or label and focus more on trends within the data instead. One of the most common applications of time series data is the idea of demand forecasting. Demand forecasting, as its name suggests, comprises the many methods and tools available to help predict demand for a given good or service ahead of time. Throughout this section, we will learn about the many aspects of time series analysis using a dataset concerning the demand forecasting of a given biotechnology product.

Treating time series data as a structured dataset

There are many different biotechnology products on the market...

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