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R Machine Learning Essentials

Book Description

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

In Detail

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.

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

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. R Machine Learning Essentials
    1. Table of Contents
    2. R Machine Learning Essentials
    3. Credits
    4. About the Author
    5. About the Reviewers
    6. www.PacktPub.com
      1. Support files, eBooks, discount offers, and more
        1. Why subscribe?
        2. Free access for Packt account holders
    7. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
        1. Downloading the example code
        2. Downloading the color images of this book
        3. Errata
        4. Citations and references
        5. Piracy
        6. Questions
    8. 1. Transforming Data into Actions
      1. A data-driven approach in business decisions
        1. Business decisions come from knowledge and expertise
        2. The digital era provides more data and expertise
        3. Technology connects data and businesses
      2. Identifying hidden patterns
        1. Data contains hidden information
        2. Business problems require hidden information
        3. Reshaping the data
        4. Identifying patterns with unsupervised learning
        5. Making business decisions with unsupervised learning
      3. Estimating the impact of an action
        1. Business problems require estimating future events
        2. Gathering the data to learn from
        3. Predicting future outcomes using supervised learning
      4. Summary
    9. 2. R – A Powerful Tool for Developing Machine Learning Algorithms
      1. Why R
        1. An interactive approach to machine learning
        2. Expectations of machine learning software
        3. R and RStudio
      2. The R tutorial
        1. The basic tools of R
        2. Understanding the basic R objects
        3. What are the R standards?
      3. Some useful R packages
      4. Summary
    10. 3. A Simple Machine Learning Analysis
      1. Exploring data interactively
        1. Defining a table with the data
        2. Visualizing the data through a histogram
        3. Visualizing the impact of a feature
        4. Visualizing the impact of two features combined
      2. Exploring the data using machine learning models
        1. Exploring the data using a decision tree
      3. Predicting newer outcomes
        1. Building a machine learning model
        2. Using the model to predict new outcomes
        3. Validating a model
      4. Summary
    11. 4. Step 1 – Data Exploration and Feature Engineering
      1. Building a machine learning solution
      2. Building the feature data
      3. Exploring and visualizing the features
      4. Modifying the features
      5. Ranking the features using a filter or a dimensionality reduction
      6. Summary
    12. 5. Step 2 – Applying Machine Learning Techniques
      1. Identifying a homogeneous group of items
        1. Identifying the groups using k-means
          1. Exploring the clusters
        2. Identifying a cluster's hierarchy
      2. Applying the k-nearest neighbor algorithm
      3. Optimizing the k-nearest neighbor algorithm
      4. Summary
    13. 6. Step 3 – Validating the Results
      1. Validating a machine learning model
        1. Measuring the accuracy of an algorithm
        2. Defining the average accuracy
        3. Visualizing the average accuracy computation
      2. Tuning the parameters
      3. Selecting the data features to include in the model
      4. Tuning features and parameters together
      5. Summary
    14. 7. Overview of Machine Learning Techniques
      1. Overview
      2. Supervised learning
        1. The k-nearest neighbors algorithm
        2. Decision tree learning
      3. Linear regression
      4. Perceptron
        1. Ensembles
      5. Unsupervised learning
        1. k-means
        2. Hierarchical clustering
        3. PCA
      6. Summary
    15. 8. Machine Learning Examples Applicable to Businesses
      1. Overview of the problem
        1. Data overview
        2. Exploring the output
        3. Exploring and transforming features
      2. Clustering the clients
      3. Predicting the output
      4. Summary
    16. Index