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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Summary


In this chapter, we studied how to deploy our machine learning models with traditional server-based deployment strategies using R's plumber, and enhanced approaches using plumber for R with Docker containers. We then studied how serverless applications can be built using cloud services and how we can easily scale applications as needed with minimal code.

We explored various web services, such as Amazon Lambda, Amazon SageMaker, and Amazon API Gateway, and studied how services can be orchestrated to deploy our machine learning model as a serverless application.

In the next chapter, we will work on a capstone project by taking up one of the latest research papers based on a real-world problem and reproducing the result.

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