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Karthik Ramasubramanian - Machine Learning Using R

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Karthik Ramasubramanian Machine Learning Using R

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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.

What Youll Learn
  • 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...
  • Karthik Ramasubramanian: author's other books


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    Karthik Ramasubramanian and Abhishek Singh Machine Learning Using R With Time - photo 1
    Karthik Ramasubramanian and Abhishek Singh
    Machine Learning Using R With Time Series and Industry-Based Use Cases in R 2nd ed.
    Karthik Ramasubramanian New Delhi Delhi India Abhishek Singh New Delhi - photo 2
    Karthik Ramasubramanian
    New Delhi, Delhi, India
    Abhishek Singh
    New Delhi, Delhi, India

    Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-4214-8 . For more detailed information, please visit http://www.apress.com/source-code .

    ISBN 978-1-4842-4214-8 e-ISBN 978-1-4842-4215-5
    https://doi.org/10.1007/978-1-4842-4215-5
    Library of Congress Control Number: 2018965407
    Karthik Ramasubramanian and Abhishek Singh 2019
    This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
    This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
    While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
    Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

    To our parents for being the guiding light and a strong pillar of support.

    And to our long friendship.

    Introduction

    In the second edition of Machine Learning Using R, we added a new chapter on time series modeling (Chapter ), which is fast emerging as a sub-field of machine learning. Apart from these two new chapters, the overall presentation of text and code in the book is put out in a new reader-friendly format.

    The new edition continues to focus on building the use cases using R, a popular statistical programming language. For topics like deep learning, it might be advised to adopt Python with frameworks like TensorFlow. However, in this new edition, we will show you how to use the R programming language with TensorFlow, hence avoiding the effort of learning Python if you are only comfortable with R.

    Like in the first edition, we have kept the fine balance of theory and application of machine learning through various real-world use cases, which give the readers a truly comprehensive collection of topics in machine leaning in one volume.

    What youll learn:
    • Understand machine learning algorithms using R

    • Master a machine learning model building a process flow

    • Theoretical foundations of machine learning algorithms

    • Industry focused real-world use cases

    • Time series modeling in R

    • Deep learning using Keras and TensorFlow in R

    Who This Book is For

    This book is for data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine learning approaches/algorithms in practice using R. The book will also benefit readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig, and Spark.

    This book is a comprehensive guide for anybody who wants to understand the machine learning model building process from end to end, including:
    • Practical demonstration of concepts in R

    • Machine learning models using Apache Hadoop and Spark

    • Time series analysis

    • Introduction to deep learning models using Keras and TensorFlow using R

    Acknowledgments

    We are grateful to our teachers, open source communities, and colleagues for enriching us with the knowledge and confidence to write the first edition of this book. Thanks to all our readers. You have made the second edition of the book possible. The knowledge in this book is an accumulation of several years of research work and professional experience gained at our alma mater and industry. We are grateful to Prof R. Nadarajan and Prof R. Anitha, Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, for their continued support and encouragement for our efforts in the field of data science.

    In the rapidly changing world, the field of machine learning is evolving very fast and most of the latest developments are driven by the open source platform. We thank all the developers and contributors across the globe who are freely sharing their knowledge. We also want to thank our colleagues our our past and current companiesSnapdeal, Deloitte, Hike, Prudential, Probyto, and Mahindra & Mahindrfor providing opportunities to experiment and create cutting-edge data science solutions.

    Karthik especially would like to thank his father, Mr. S Ramasubramanian, for always being a source of inspiration in his life. He is immensely thankful to his supervisor, Mr. Nikhil Dwarakanath, director of the data science team at Snapdeal, for creating the opportunities to bring about the best analytics professional in him and providing the motivation to take up challenging projects.

    Abhishek would like to thank his father, Mr. Charan Singh, a senior scientist in the India meteorological department, for introducing him to the power of data in weather forecasting in his formative years. On a personal front, Abhishek would like to thank his mother Jaya, sister Asweta, and brother Avilash, for their continued moral support.

    We want to thank our publisher Apress, specifically Celestine, for proving us with this opportunity, Sanchita Prachi for managing the first edition of the book, and Aditee Mirashi for the second edition, Poonam and Piyush for their reviews, and everybody involved in the production team.

    Karthik Ramasubramanian

    Abhishek Singh

    Table of Contents
    About the Authors and About the Technical Reviewer
    About the Authors
    Karthik Ramasubramanian
    has over seven years of practice and leading data science and business - photo 3
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