Machine Learning Using R : with Time Series and Industry Based Use Cases in R 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. You will: 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 Chapter 1: Introduction to Machine Learning Chapter 2: Data Exploration and Preparation Chapter 3: Sampling and Resampling Techniques Chapter 4: Visualization of Data Chapter 5: Feature Engineering Chapter 6: Machine Learning Models: Theory and Practice Chapter 7: Machine Learning Model Evaluation Chapter 8: Model Performance Improvement Chapter 9: Time Series Modelling Chapter 10: Scalable Machine Learning and related technology Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow