Machine Learning Using R
With Time Series and Industry-Based Use Cases in R
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
| Author: | Karthik Ramasubramanian, Abhishek Singh | ISBN: | 9781484242155 |
| Publisher: | Apress | Publication: | December 12, 2018 |
| Imprint: | Apress | Language: | English |
| Author: | Karthik Ramasubramanian, Abhishek Singh |
| ISBN: | 9781484242155 |
| Publisher: | Apress |
| Publication: | December 12, 2018 |
| Imprint: | Apress |
| Language: | English |
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.
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.
