Examine the latest technological advancements in building a scalable machinelearning model with big data using R. This second edition shows you how to work with a machinelearning 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 realworld usecases 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 machinelearning models

Cover the theoretical foundations of machinelearning algorithms

See industry focused realworld 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 machinelearning approaches/algorithms in practice using R.