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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

Building Serverless ML Applications


Serverless computing is the new paradigm within cloud computing. It allows us to build and run applications and services without thinking about servers. In reality, the application we build still runs on a cloud server, but the entire process for server management is done by the cloud service provider, such as AWS. By leveraging the serverless platform, we can build and deploy robust, large-scale, complex applications by only focusing on our application code instead of worrying about provisioning, configuring, and managing servers.

We have explored some important components of the AWS serverless platform such as AWS Lambda in this chapter, and we can now leverage these solutions to build a machine learning application where we can only focus on the core ML code and forget about provisioning infrastructure and scaling applications.

Exercise 101: Building a Serverless Application Using API Gateway, AWS Lambda, and SageMaker

In this exercise, we will build a...

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