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  • Book Overview & Buying R Machine Learning Essentials
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R Machine Learning Essentials

R Machine Learning Essentials

By : Michele Usuelli
5 (1)
Buy this Book
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R Machine Learning Essentials

R Machine Learning Essentials

5 (1)
By: Michele Usuelli
Buy this Book

Overview of this book

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?
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Transforming Data into Actions
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1. Transforming Data into Actions
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A data-driven approach in business decisions
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Identifying hidden patterns
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Estimating the impact of an action
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Summary
2
2. R – A Powerful Tool for Developing Machine Learning Algorithms
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2. R – A Powerful Tool for Developing Machine Learning Algorithms
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Why R
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The R tutorial
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Some useful R packages
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Summary
3
3. A Simple Machine Learning Analysis
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3. A Simple Machine Learning Analysis
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Exploring data interactively
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Exploring the data using machine learning models
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Predicting newer outcomes
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Summary
4
4. Step 1 – Data Exploration and Feature Engineering
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4. Step 1 – Data Exploration and Feature Engineering
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Building a machine learning solution
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Building the feature data
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Exploring and visualizing the features
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Modifying the features
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Ranking the features using a filter or a dimensionality reduction
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Summary
5
5. Step 2 – Applying Machine Learning Techniques
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5. Step 2 – Applying Machine Learning Techniques
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Identifying a homogeneous group of items
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Applying the k-nearest neighbor algorithm
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Optimizing the k-nearest neighbor algorithm
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Summary
6
6. Step 3 – Validating the Results
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6. Step 3 – Validating the Results
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Validating a machine learning model
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Tuning the parameters
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Selecting the data features to include in the model
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Tuning features and parameters together
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Summary
7
7. Overview of Machine Learning Techniques
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7. Overview of Machine Learning Techniques
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Overview
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Supervised learning
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Linear regression
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Perceptron
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Unsupervised learning
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Summary
8
8. Machine Learning Examples Applicable to Businesses
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8. Machine Learning Examples Applicable to Businesses
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Overview of the problem
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Clustering the clients
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Predicting the output
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Summary
9
Index
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Index

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Load the randomForest package containing the random forest algorithm."

A block of code is set as follows:

[default]
arrayFeatures <- names(dtBank)
arrayFeatures <- arrayFeatures[arrayFeatures != 'output']
formulaAll <- paste('output', '~')
formulaAll <- paste(formulaAll, arrayFeatures[1])
for(nameFeature in arrayFeatures[-1]){
  formulaAll <- paste(formulaAll, '+', nameFeature)
}
formulaAll <- formula(formulaAll)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

n1 + n2
[1] 5
n1 * n2
[1] 6

New terms and important words are shown in bold.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Tech Concepts
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Programming languages
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R Machine Learning Essentials
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