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Learning Bayesian Models with R

You're reading from Learning Bayesian Models with R Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

Product type Paperback

Published in Oct 2015

Publisher Packt

ISBN-13 9781783987603

Length 168 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Author (1):

Hari Manassery Koduvely

View More author details

Table of Contents (11) Chapters

Preface

1. Introducing the Probability Theory FREE CHAPTER

2. The R Environment

3. Introducing Bayesian Inference

4. Machine Learning Using Bayesian Inference

5. Bayesian Regression Models

6. Bayesian Classification Models

7. Bayesian Models for Unsupervised Learning

8. Bayesian Neural Networks

9. Bayesian Modeling at Big Data Scale

Index

Computing clusters on the cloud

In order to process large datasets using Hadoop and associated R packages, one needs a cluster of computers. In today's world, it is easy to get using cloud computing services provided by Amazon, Microsoft, and others. One needs to pay only for the amount of CPU and storage used. No need for upfront investments on infrastructure. The top four cloud computing services are AWS by Amazon, Azure by Microsoft, Compute Cloud by Google, and Bluemix by IBM. In this section, we will discuss running R programs on AWS. In particular, you will learn how to create an AWS instance; install R, RStudio, and other packages in that instance; develop and run machine learning models.

Amazon Web Services

Popularly known as AWS, Amazon Web Services started as an internal project in Amazon in 2002 to meet the dynamic computing requirements to support their e-commerce business. This grew as an infrastructure as a service and in 2006 Amazon launched two services to the world, Simple...

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Authors (1)

Hari Manassery Koduvely

Hari Manassery Koduvely

Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.

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