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Hands-On Data Science with R

You're reading from   Hands-On Data Science with R Techniques to perform data manipulation and mining to build smart analytical models using R

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Length 420 pages
Edition 1st Edition
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Authors (4):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Author Profile Icon Doug Ortiz
Doug Ortiz
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Author Profile Icon Ricardo Anjoleto Farias
Ricardo Anjoleto Farias
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Data Science and R FREE CHAPTER 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Markovian in R


"Statistics is the grammar of science."
– Karl Pearson

Markovian-type models are yet another option to exercise pattern discovery. The idea behind Hidden Markovian Models (HMMs) is both clever and very useful. Although some people would only recognize them for their ability to model time series, HMMs are also suitable for things such as speech recognition and computer vision.

In this chapter, readers will find a brief review of Markovian models and the HMM, a discussion about where HMMs can be applied, and of course, a practical guide, teaching the nuts and bolts of deploying HMMs through R.

In this chapter, we will cover the following topics:

  • Markovian models basics
  • Hidden Markovian Models (HMMs) basics
  • Incorporate information about the past...
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