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Machine Learning Using R Paperback – December 24, 2016

4.2 4.2 out of 5 stars 5 ratings

There is a newer edition of this item:

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 LearnUse the model building process flowApply theoretical aspects of machine learningReview industry-based cae studiesUnderstand ML algorithms using RBuild machine learning models using Apache Hadoop and SparkWho This Book is Fo rData 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.

Product details

  • Publisher ‏ : ‎ Apress; 1st ed. edition (December 24, 2016)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 592 pages
  • ISBN-10 ‏ : ‎ 1484223330
  • ISBN-13 ‏ : ‎ 978-1484223338
  • Item Weight ‏ : ‎ 19.47 pounds
  • Dimensions ‏ : ‎ 6.1 x 1.34 x 9.25 inches
  • Customer Reviews:
    4.2 4.2 out of 5 stars 5 ratings

About the author

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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

Reviewed in the United States on May 19, 2017
Good volume. Solid material & code.
Reviewed in the United States on December 28, 2017
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.
Reviewed in the United States on November 1, 2017
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.
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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
Good book!