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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)
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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
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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
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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
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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
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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
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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
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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
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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
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Index
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Index

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...

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Tech Concepts
36
Programming languages
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R Machine Learning Essentials
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