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Karthik Ramasubramanian
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Machine Learning Using R 1st ed. Edition

by Karthik Ramasubramanian (Author), Abhishek Singh (Author)

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.
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  1. ISBN-10
    1484223330
  2. ISBN-13
    978-1484223338
  3. Edition
    1st ed.
  4. Publication date
    December 24, 2016
  5. Language
    English
  6. Dimensions
    6.14 x 1.2 x 9.21 inches
  7. Print length
    566 pages
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Machine Learning Using R: With Time Series and Industry-Based Use Cases in R
Machine Learning Using R: With Time Series and Industry-Based Use Cases in R
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Editorial Reviews

Review

“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)

From the Back Cover

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.

Product details

  • Publisher ‏ : ‎ Apress
  • Publication date ‏ : ‎ December 24, 2016
  • Edition ‏ : ‎ 1st ed.
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 566 pages
  • ISBN-10 ‏ : ‎ 1484223330
  • ISBN-13 ‏ : ‎ 978-1484223338
  • Item Weight ‏ : ‎ 1.85 pounds
  • Dimensions ‏ : ‎ 6.14 x 1.2 x 9.21 inches
  • Customer Reviews:

About the author

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Karthik Ramasubramanian

Karthik Ramasubramanian

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Karthik has over eight years of practice and experience in leading data science function in retail, FMCG, e-commerce, information technology, and hospitality sector for multi-national companies and unicorn startups. A researcher, the author of four books, and a problem solver with a diverse set of experience in the data science lifecycle, starting from a data problem discovery to creating a data science prototype/product.

On the descriptive side of data science, designed, developed, and spearheaded many A/B experiment frameworks for improving product features, conceptualized funnel analysis for understanding user interactions, and identifying the friction points within a product, designing statistically robust metrics and visual dashboards. On the predictive side, developed intelligent chatbots that understand human-like interactions, customer segmentation models, recommendation systems, identifying medical specialization from a patient query for telemedicine, and many more.

Actively participate in analytics related thought leadership, authoring blogs & books, public speaking, meet-ups, and training & mentoring for Data Science.

Industry Expertise: Consumer Products, E-Commerce, Information Technology, and Big Data Analytics

Current areas of interest: ROI driven data product development, Machine Learning Algorithms, Data Product Frameworks, Internet of Things (IoT), Scalable Data Platforms

Customer reviews

4.2 out of 5 stars
5 global ratings

Top reviews from the United States

  • Duncan W. Robinson
    5.0 out of 5 stars Good volume. Solid material & code
    Reviewed in the United States on May 19, 2017
    Format: PaperbackVerified Purchase
    Good volume. Solid material & code.
    Helpful
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  • CO
    4.0 out of 5 stars Practical overview of machine learning using R
    Reviewed in the United States on December 28, 2017
    Format: PaperbackVerified Purchase
    This is a practical book especially for machine learning practitioners who are somewhat experienced in R. It provides an overview of different approaches and the code provided in the book is helpful for trying out multiple techniques on a given data set. I like the fact that the last chapter briefly covers Hadoop and Spark. Even though the book could have been edited better, it is pretty comprehensive and I appreciate overall effort from the authors.
    Helpful
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  • Jeffery W.
    2.0 out of 5 stars Good start, poor ending
    Reviewed in the United States on November 1, 2017
    Format: Paperback
    I would like to give a much better score for the first half of this book (namely, Chapter 1-6.5), which provides a comprehensive introduction to the background knowledge for machine learning. But the later part is a total disappointment, filled with copy-pasted documents, corrupted code snippets, and confusing explanations.
    One person found this helpful
    Helpful
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Top reviews from other countries

  • Amazon Customer
    4.0 out of 5 stars Four Stars
    Reviewed in Canada on June 22, 2017
    Format: PaperbackVerified Purchase
    Good book!
    Report