R Machine Learning Essentials

Gain quick access to the machine learning concepts and practical applications using the R development environment

R Machine Learning Essentials

This ebook is included in a Mapt subscription
Michele Usuelli

1 customer reviews
Gain quick access to the machine learning concepts and practical applications using the R development environment
$10.00
$39.99
RRP $23.99
RRP $39.99
eBook
Print + eBook
Preview in Mapt

Book Details

ISBN 139781783987740
Paperback218 pages

Book Description

R Machine Learning Essentials provides you with an introduction to machine learning with R. Machine learning finds its applications in speech recognition, search-based operations, and artificial intelligence, among other things. You will start off by getting an introduction to what machine learning is, along with some examples to demonstrate the importance in understanding the basic ideas of machine learning. This book will then introduce you to R and you will see that it is an influential programming language that aids effective machine learning. You will learn the three steps to build an effective machine learning solution, which are exploring the data, building the solution, and validating the results. The book will demonstrate each step, highlighting their purpose and explaining techniques related to them.

By the end of this book, you will be able to use the machine learning techniques effectively, identify business problems, and solve them by applying appropriate solutions.

Table of Contents

Chapter 1: Transforming Data into Actions
A data-driven approach in business decisions
Identifying hidden patterns
Estimating the impact of an action
Summary
Chapter 2: R – A Powerful Tool for Developing Machine Learning Algorithms
Why R
The R tutorial
Some useful R packages
Summary
Chapter 3: A Simple Machine Learning Analysis
Exploring data interactively
Exploring the data using machine learning models
Predicting newer outcomes
Summary
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: Overview of Machine Learning Techniques
Overview
Supervised learning
Linear regression
Perceptron
Unsupervised learning
Summary
Chapter 8: Machine Learning Examples Applicable to Businesses
Overview of the problem
Clustering the clients
Predicting the output
Summary

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

Authors

Table of Contents

Chapter 1: Transforming Data into Actions
A data-driven approach in business decisions
Identifying hidden patterns
Estimating the impact of an action
Summary
Chapter 2: R – A Powerful Tool for Developing Machine Learning Algorithms
Why R
The R tutorial
Some useful R packages
Summary
Chapter 3: A Simple Machine Learning Analysis
Exploring data interactively
Exploring the data using machine learning models
Predicting newer outcomes
Summary
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: Overview of Machine Learning Techniques
Overview
Supervised learning
Linear regression
Perceptron
Unsupervised learning
Summary
Chapter 8: Machine Learning Examples Applicable to Businesses
Overview of the problem
Clustering the clients
Predicting the output
Summary

Book Details

ISBN 139781783987740
Paperback218 pages
Read More
From 1 reviews

Read More Reviews