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Machine learning using Rwith time se...

Ramasubramanian, Karthik.

Machine learning using Rwith time series and industry-based use cases in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning using Rby Karthik Ramasubramanian, Abhishek Singh.
Reminder of title:	with time series and industry-based use cases in R /
Author:	Ramasubramanian, Karthik.
other author:	Singh, Abhishek.
Published:	Berkeley, CA :Apress :2019.
Description:	xxiv, 700 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-4215-5
ISBN:	9781484242155$q(electronic bk.)

Machine learning using Rwith time series and industry-based use cases in R /
Ramasubramanian, Karthik.

Machine learning using Rwith time series and industry-based use cases in R /[electronic resource] :by Karthik Ramasubramanian, Abhishek Singh. - 2nd ed. - Berkeley, CA :Apress :2019. - xxiv, 700 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484242155$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-4215-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .R363 2019

Dewey Class. No.: 006.31

Machine learning using Rwith time series and industry-based use cases in R /
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505 0 $a 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.
520 $a 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.
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