Machine Learning Using R: With Time Series and Industry-Based Use Cases in R

by Karthik Ramasubramanian, Abhishek Singh

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 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.

724 pages, Paperback

Published December 13, 2018

Reviews

[Ferhat Culfaz · Rating 2 out of 5]

Poorly written, bad descriptions of the key concepts, diagrams are also not up to scratch. Mainly has code dump and output.

Really only use it to see examples of applications of different R packages for machine learning. Definitely not a reference text.