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. What You'll Learn 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 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. Front Matter ....Pages i-xxiv Introduction to Machine Learning and R (Karthik Ramasubramanian, Abhishek Singh)....Pages 1-33 Data Preparation and Exploration (Karthik Ramasubramanian, Abhishek Singh)....Pages 35-77 Sampling and Resampling Techniques (Karthik Ramasubramanian, Abhishek Singh)....Pages 79-150 Data Visualization in R (Karthik Ramasubramanian, Abhishek Singh)....Pages 151-209 Feature Engineering (Karthik Ramasubramanian, Abhishek Singh)....Pages 211-251 Machine Learning Theory and Practice (Karthik Ramasubramanian, Abhishek Singh)....Pages 253-481 Machine Learning Model Evaluation (Karthik Ramasubramanian, Abhishek Singh)....Pages 483-531 Model Performance Improvement (Karthik Ramasubramanian, Abhishek Singh)....Pages 533-593 Time Series Modeling (Karthik Ramasubramanian, Abhishek Singh)....Pages 595-627 Scalable Machine Learning and Related Technologies (Karthik Ramasubramanian, Abhishek Singh)....Pages 629-665 Deep Learning Using Keras and TensorFlow (Karthik Ramasubramanian, Abhishek Singh)....Pages 667-688 Back Matter ....Pages 689-700 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