Machine Learning Using R

With Time Series and Industry-Based Use Cases in R

By: Abhishek Singh, Karthik Ramasubramanian

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Paperback | 13 December 2018 | Edition Number 2

At a Glance

Paperback
726 Pages

Edition Type
Revised

Dimensions(cm)
25.4 x 17.78 x 3.81

Paperback

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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R.

As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning.

What You'll Learn

Understand machine learning algorithms using R
Master the process of building machine-learning models
Cover the theoretical foundations of machine-learning algorithms
See industry focused real-world use cases
Tackle time series modeling in R
Apply deep learning using Keras and TensorFlow in R

Who This Book is For

Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.

Industry Reviews

"The wide variety of concepts and the unique blend of theory and exercises recommend this book as a reliable starting point for researchers looking for a deeper understanding of machine learning approaches ... . The book is suitable for a wide variety of backgrounds and skill sets, it is addressed to researchers from undergraduates to postgraduates and established researchers and from a wide range of interdisciplinary backgrounds such as computer science, mathematics, physics and biology." (Irina Ioana Mohorianu, zbMATH 1423.68007, 2019)

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