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R Machine Learning Essentials
R Machine Learning Essentials

R Machine Learning Essentials: Gain quick access to the machine learning concepts and practical applications using the R development environment

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R Machine Learning Essentials

Chapter 2. R – A Powerful Tool for Developing Machine Learning Algorithms

Before using the machine learning techniques, we need to choose the proper software. There are many programming languages and types of software that provide the user with machine learning tools. In fact, the most important part is knowing which techniques to use and how to build new ones, and the software is just a tool. However, choosing the right software allows you to build faster and more accurate solutions.

In this chapter, you will learn:

  • The software requirements for building a machine learning solution
  • How R, combined with RStudio, facilitates the development of a machine learning solution
  • The structure of R
  • The tools of R
  • Some important R packages

Why R

Understanding the challenges in developing machine learning solutions helps in choosing the software that allows you to face them in the easiest and most effective way. This chapter illustrates the software requirements and explains why we will use R.

An interactive approach to machine learning

Developing a machine learning solution consists of steps that have different requirements. In addition, the result of a step helps in improving the previous, so it's often necessary to come back to modify it.

Before facing a problem and building its machine learning solution, we want to know as much as possible about the challenge and the available resources. Effectively, it's very important to have all the possible information in order to define the right path to the solution. For this purpose, starting from the data, we extract business insights and patterns from the data using statistical and machine learning tools.

A simple example is a big supermarket that launches a new marketing...

The R tutorial

I assume that you are already familiar with a programming language, although not necessarily R. This section contains a brief R tutorial that shows some tools that are useful for building machine learning solutions. Since a proper introduction to R would require an entire book, this tutorial focuses on just some relevant topics.

If you're already familiar with R, you can quickly read through this section. If you're new to R, I advise you to combine this section with an interactive online tutorial to get a more complete overview. In addition, it'll be very useful to play with the tools in order to acquire more familiarity.

Before starting with the tutorial, we need to install R and RStudio. Both types of software are open source, and they support the most relevant operating systems. It's also useful to read the RStudio tutorial to understand how to use this powerful IDE.

My advice is to generate a new R script in the RStudio environment and to copy and paste...

Some useful R packages

There are different R packages that provide users with general-purpose functions and specific techniques. This chapter introduces two powerful general purpose packages: data.table and plyr.

Some packages are already installed in the basic version of R. However, in order to use data.table and plyr, we need to download them from the official CRAN repository using install.packages. Let's start with data.table, which is a package that provides additional tools used to deal with data frames:

install.packages('data.table')

If the command doesn't work, you can specify the repository:

install.packages(
  pkgs = 'data.table',
  repos = 'http://cran.us.r-project.org'
)

After installing the package, we need to load it in order to use its functions. Unfortunately, R will import all the functions from the package without using a namespace, and sometimes there might be name conflicts across different packages:

library(data.table)

The package contains...

Summary

In this chapter, you understood the software required to develop a machine learning solution. You saw why R, combined with RStudio, is a good tool to use to help you overcome machine learning challenges.

You learned about the basics of R and some of the most important data types and functions. You have also seen packages such as data.table and plyr.

The next chapter shows you a simple example of a challenge that can be faced using exploratory data analysis and machine learning. You will see R tools used to build charts and use machine learning algorithms.

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Description

If your company is like most, the number one reason that projects have failed over the years don’t have to do with technology.  They have to do with people.  People didn’t like the new technology.  People weren’t trained properly on the change.  People hadn’t received adequate communications and didn’t understand the change.  Sound familiar?

What you will learn

  • Introduce yourself to the basics of machine learning and R
  • Develop an interactive data analysis with R to get insights into the data
  • Explore business problems and identify key features that are highly relevant to the solution
  • Build machine learning algorithms using the most powerful tools in R
  • Apply different machine learning techniques for different kinds of business problems
  • Validate the results of the techniques and identify the best solution to a problem
  • Identify business problems and solve them by developing effective solutions
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 28, 2014
Length: 218 pages
Edition : 1st
Language : English
ISBN-13 : 9781783987740
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Table of Contents

9 Chapters
1. Transforming Data into Actions Chevron down icon Chevron up icon
2. R – A Powerful Tool for Developing Machine Learning Algorithms Chevron down icon Chevron up icon
3. A Simple Machine Learning Analysis Chevron down icon Chevron up icon
4. Step 1 – Data Exploration and Feature Engineering Chevron down icon Chevron up icon
5. Step 2 – Applying Machine Learning Techniques Chevron down icon Chevron up icon
6. Step 3 – Validating the Results Chevron down icon Chevron up icon
7. Overview of Machine Learning Techniques Chevron down icon Chevron up icon
8. Machine Learning Examples Applicable to Businesses Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Luca Massaron Oct 27, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been using R since 2002 for a large variety of projects in marketing research, analytics and data science, but now I use it much rarely. In the recent years my data science projects as a practitioner required me to strongly concentrate on using Python.That's why, the last week, when helping a colleague of mine in a project she is running with R, I needed to quickly check for the best ways in R to do exploratory analysis, build a few models, validate and test them, report the results. And I needed some help for that myself, because I indeed couldn't remember all the details and subtleties from the R language and its libraries!Michele Usuelli's book came in great aid in that. Michele is the Lead Data Scientist at Microsoft UK and he uses R everyday for solving data science problems for a variety of different customers. In his book he concentrated all the essentials you need to run your project using R he got from his experience. As I skimmed through the book I could find everything that I needed: loading data, data exploration, feature manipulation, training of a model, tuning and validation, error measures. Everything you need for running your project is there, in an essential form but written in clear words and with ready to be used code snippets.Being an author myself (of Python books!), I much appreciated Michele's book and I personally recommend it to any practitioner in need of being able to fast set up or validate a project of her/his own or for her/his data science team.
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