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

<p></p><p>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.</p><p>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.</p><b>What You'll Learn </b><p></p><p></p><ul><li>Understand machine learning algorithms using R<br></li><li>Master the process of building machine-learning models <br></li><li>Cover the theoretical foundations of machine-learning algorithms<br></li><li>See industry focused real-world use cases<br></li><li>Tackle time series modeling in R<br></li><li>Apply deep learning using Keras and TensorFlow in R<br></li></ul><p></p><p><b>Who This Book is For</b></p><p>Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.</p>