Machine Learning Using R
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Beschreibung
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.
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 flow
- Apply theoretical aspects of machine learning
- Review industry-based cae studies
- Understand ML algorithms using R
- Build machine learning models using Apache Hadoop and Spark
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 along with ways to see them in practice using R.
The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.
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
Autor*in
Karthik Ramasubramanian
Themen in »Machine Learning Using R«
Machine Learning Data Exploration Sampling Techniques Data Visualization Feature Engineering Machine Learning Models Scalable Machine Learning
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“This is a fantastic and commendable effort by the authors to write a comprehensive book on machine learning. They have taken special care to provide complete R software code while discussing machine learning concepts and use cases. While there are plenty of resources on the Internet about machine learning, this book will serve as a single-source reference for both theoretical and practical machine learning leveraging R.” (Computing Reviews, October, 2017)
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