Machine Learning Using R: With Time Series and Industry-Based Use Cases in R by Karthik Ramasubramanian, Abhishek Singh
English | December 13, 2018 | ISBN: 1484242149 | 724 pages | MOBI | 22 Mb
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 RMaster the process of building machine-learning modelsCover the theoretical foundations of machine-learning algorithmsSee industry focused real-world use casesTackle time series modeling in RApply 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.
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