Learning Bayesian Models with R ePub (Adobe DRM) download by Dr. Hari M. Koduvely

Learning Bayesian Models with R

Packt Publishing
Publication date: October 2015
ISBN: 9781783987610
Digital Book format: ePub (Adobe DRM)

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About This Book
  • Understand the principles of Bayesian Inference with less mathematical equations
  • Learn state-of-the art Machine Learning methods
  • Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide
Who This Book Is For

This book is intended for data scientists who analyze large datasets to generate insights and for data engineers who develop platforms, solutions or applications based on machine learning. Though many data science practitioners are quite familiar with machine learning techniques and R, they may not know about Bayesian inference and its merits. This book therefore would be helpful to even experienced data scientists and data engineers to learn Bayesian methods and use them in their projects.

What You Will Learn
  • How machine learning models are built using Bayesian inference techniques
  • Perform Bayesian inference using the R programming language
  • State-of-the-art R packages for Bayesian models and how to apply them in data science problems.
  • Understand Bayesian models for deep learning
  • Use of R in Big Data frameworks such as Hadoop and Spark
  • Run R programs in cloud computing environments such as AWS and Azure
In Detail

Bayesian inference provides a unified framework to deal with all sorts of uncertainties when learning patterns from data using machine learning models for predicting future observations. With the recent advances in computation and the availability of several open source packages in R, Bayesian modeling has become more feasible to use for practical applications.

Learning Bayesian Models with R starts by giving you comprehensive coverage of the Bayesian machine learning models and the R packages that implement them. Every chapter begins with a theoretical description of the method, explained in a very simple manner. Then, relevant R packages are discussed and some illustrations that use datasets from the UCI machine learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.

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