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Machine learning using R / by Karthik Ramasubramanian, Abhishek Singh.

By: Ramasubramanian, Karthik [author. ].
Contributor(s): Singh, Abhishek [author.] | SpringerLink (Online service).
Material type: TextTextPublisher: Berkeley, CA : Apress : Imprint: Apress, 2017Description: xxiii, 566 pages : illustrations ; 23 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781484223338.Other title: Machine Learning Using R : A Comprehensive Guide to Machine Learning.Subject(s): Computer science | Computer programming | Programming languages (Electronic computers) | Database management | ComputersDDC classification: 006 Other classification: Pu Online resources: Table of Contents / Abstracts | Tillgñglig fr̲ anvñdare inom Stockholms universitet SpringerLink Books Professional And Applied Computing 2017:Full Text | Extern tillgn̄g endast anstl̃lda och studenter vid LiU Books 24x7 IT Pro Collection | Student portal login Springer DDA:Full Text | Online access for KTHB fulltext SpringerLink Books Professional And Applied Computing 2017
Contents:
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.-.
Summary: 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.
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Includes index.

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.

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