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R Machine Learning Essentials

By : Michele Usuelli
Book Image

R Machine Learning Essentials

By: Michele Usuelli

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R Machine Learning Essentials
Michele Usuelli
Nov, 2014 | 218 pages
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Table of Contents (15 chapters)
R Machine Learning Essentials
R Machine Learning Essentials
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Transforming Data into Actions
Transforming Data into Actions
A data-driven approach in business decisions
Identifying hidden patterns
Estimating the impact of an action
Summary
2
R – A Powerful Tool for Developing Machine Learning Algorithms
R – A Powerful Tool for Developing Machine Learning Algorithms
Why R
The R tutorial
Some useful R packages
Summary
3
A Simple Machine Learning Analysis
A Simple Machine Learning Analysis
Exploring data interactively
Exploring the data using machine learning models
Predicting newer outcomes
Summary
4
Step 1 – Data Exploration and Feature Engineering
Step 1 – Data Exploration and Feature Engineering
Building a machine learning solution
Building the feature data
Exploring and visualizing the features
Modifying the features
Ranking the features using a filter or a dimensionality reduction
Summary
5
Step 2 – Applying Machine Learning Techniques
Step 2 – Applying Machine Learning Techniques
Identifying a homogeneous group of items
Applying the k-nearest neighbor algorithm
Optimizing the k-nearest neighbor algorithm
Summary
6
Step 3 – Validating the Results
Step 3 – Validating the Results
Validating a machine learning model
Tuning the parameters
Selecting the data features to include in the model
Tuning features and parameters together
Summary
7
Overview of Machine Learning Techniques
Overview of Machine Learning Techniques
Overview
Supervised learning
Linear regression
Perceptron
Unsupervised learning
Summary
8
Machine Learning Examples Applicable to Businesses
Machine Learning Examples Applicable to Businesses
Overview of the problem
Clustering the clients
Predicting the output
Summary
Index
Index

Chapter 1. Transforming Data into Actions

To face a business problem, we need the knowledge and expertise to find its solution. In addition, we also require related data that will help in identifying its solution. This chapter shows how new technologies allow us to build powerful machines that learn from data to give support to business decisions.

The topics that will be covered in this chapter are as follows:

  • A general idea for approaching business problems

  • The new challenges relating to digital technologies

  • How the new tools help in using information

  • How the tools identify the information that is not evident

  • How the tools can estimate the outcome of future events

  • Why R?

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