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Big Data Analytics with R

You're reading from   Big Data Analytics with R Leverage R Programming to uncover hidden patterns in your Big Data

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781786466457
Length 506 pages
Edition 1st Edition
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Author (1):
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Simon Walkowiak Simon Walkowiak
Author Profile Icon Simon Walkowiak
Simon Walkowiak
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Table of Contents (10) Chapters Close

Preface 1. The Era of Big Data FREE CHAPTER 2. Introduction to R Programming Language and Statistical Environment 3. Unleashing the Power of R from Within 4. Hadoop and MapReduce Framework for R 5. R with Relational Database Management Systems (RDBMSs) 6. R with Non-Relational (NoSQL) Databases 7. Faster than Hadoop - Spark with R 8. Machine Learning Methods for Big Data in R 9. The Future of R - Big, Fast, and Smart Data

What is machine learning?


We will begin this chapter with a brief introduction to the concept of machine learning by presenting an overview of the most frequently used predictive algorithms, their classification, and typical characteristics. We will also list a number of resources where you can find more information about the specifics of chosen algorithms and we will guide you through the growing number of Big Data machine learning tools available to data scientists.

Machine learning algorithms

Machine learning methods encapsulate data mining and statistical techniques allowing researchers to make sense of data, model the relationships between variables or features, and extend these models to predict the values or classes of events in the future. So how does this field differ from the already well-known statistical testing? In general, we can say that machine learning methods are less stringent about the required format and characteristics of the data; that is, many machine learning algorithms...

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