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Titlebook: Machine Learning Using R; Karthik Ramasubramanian,Abhishek Singh Book 20171st edition Karthik Ramasubramanian and Abhishek Singh 2017 Mach

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书目名称Machine Learning Using R
编辑Karthik Ramasubramanian,Abhishek Singh
视频video
概述A comprehensive guide for anybody who wants to understand ML model building process end to end.Also covers scalable machine learning.Practical demonstration of concepts in R
图书封面Titlebook: Machine Learning Using R;  Karthik Ramasubramanian,Abhishek Singh Book 20171st edition Karthik Ramasubramanian and Abhishek Singh 2017 Mach
描述.Examine the latest technological advancements in building a scalable machine learning model with Big Data using R. This book shows you how to work with a machine learning algorithm and use it to build a ML model from raw data...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. 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 the images are available in color and hi-res as part of the code download...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 this book makes it easy for someone to connect the dots...What You‘ll Learn .Use the model building process fl
出版日期Book 20171st edition
关键词Machine Learning; Data Exploration; Sampling Techniques; Data Visualization; Feature Engineering; Machine
版次1
doihttps://doi.org/10.1007/978-1-4842-2334-5
isbn_ebook978-1-4842-2334-5
copyrightKarthik Ramasubramanian and Abhishek Singh 2017
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Book 20171st editionto 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 this book makes it easy for someone to connect the dots...What You‘ll Learn .Use the model building process fl
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Feature Engineering,n easy-to-use guide of key terms and methodology used in feature engineering. The chapter will give due weight to the domain knowledge and some common business limitations while using machine learning algorithms to solve business problems.
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