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Machine learning using R

Ramasubramanian, Karthik.

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Machine learning using R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning using R/ by Karthik Ramasubramanian, Abhishek Singh.
Author:	Ramasubramanian, Karthik.
other author:	Singh, Abhishek.
Published:	Berkeley, CA :Apress : : 2017.,
Description:	xxiii, 566 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Machine Learning and R -- Chapter 2: Data Preparation and Exploration -- 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: Scalable Machine Learning and related technology.-.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-1-4842-2334-5
ISBN:	9781484223345

Machine learning using R
Ramasubramanian, Karthik.

Machine learning using R[electronic resource] /by Karthik Ramasubramanian, Abhishek Singh. - Berkeley, CA :Apress :2017. - xxiii, 566 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Machine Learning and R -- Chapter 2: Data Preparation and Exploration -- 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: Scalable Machine Learning and related technology.-.

This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data. This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots. For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.

ISBN: 9781484223345

Standard No.: 10.1007/978-1-4842-2334-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning using R
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100 1 $a Ramasubramanian, Karthik. $3 3219972
245 1 0 $a Machine learning using R $h [electronic resource] / $c by Karthik Ramasubramanian, Abhishek Singh.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2017.
300 $a xxiii, 566 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to Machine Learning and R -- Chapter 2: Data Preparation and Exploration -- 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: Scalable Machine Learning and related technology.-.
520 $a This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data. This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots. For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.
650 0 $a Machine learning. $3 533906
650 0 $a R (Computer program language) $3 784593
650 0 $a Computer science. $3 523869
650 0 $a Computer programming. $3 527209
650 0 $a Programming languages (Electronic computers) $3 606806
650 0 $a Database management. $3 527442
650 0 $a Computers. $3 544777
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Computing Methodologies. $3 830243
650 2 4 $a Programming Techniques. $3 892496
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Database Management. $3 891010
700 1 $a Singh, Abhishek. $3 901091
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856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-2334-5
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