Mastering Machine Learning with R - Second Edition

Master machine learning techniques with R to deliver insights in complex projects
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Mastering Machine Learning with R - Second Edition

Cory Lesmeister

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Master machine learning techniques with R to deliver insights in complex projects
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Book Details

ISBN 139781787287471
Paperback420 pages

Book Description

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.

You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.

With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

Table of Contents

Chapter 1: A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
Chapter 3: Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Discriminant analysis overview
Multivariate Adaptive Regression Splines (MARS)
Model selection
Summary
Chapter 4: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Regularization and classification
Summary
Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Business case
Feature selection for SVMs
Summary
Chapter 6: Classification and Regression Trees
An overview of the techniques
Business case
Summary
Chapter 7: Neural Networks and Deep Learning
Introduction to neural networks
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
Chapter 8: Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Random forest
Business understanding
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 9: Principal Components Analysis
An overview of the principal components
Business understanding
Modeling and evaluation
Summary
Chapter 10: Market Basket Analysis, Recommendation Engines, and Sequential Analysis
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
Business understanding and recommendations
Data understanding, preparation, and recommendations
Modeling, evaluation, and recommendations
Sequential data analysis
Summary
Chapter 11: Creating Ensembles and Multiclass Classification
Ensembles
Business and data understanding
Modeling evaluation and selection
Multiclass classification
Business and data understanding
Model evaluation and selection
MLR's ensemble
Summary
Chapter 12: Time Series and Causality
Univariate time series analysis
Business understanding
Modeling and evaluation
Summary
Chapter 13: Text Mining
Text mining framework and methods
Topic models
Business understanding
Modeling and evaluation
Summary
Chapter 14: R on the Cloud
Creating an Amazon Web Services account
Summary
Chapter 15: R Fundamentals
Getting R up-and-running
Using R
Data frames and matrices
Creating summary statistics
Installing and loading R packages
Data manipulation with dplyr
Summary
Chapter 16: Sources

What You Will Learn

  • Gain deep insights into the application of machine learning tools in the industry
  • Manipulate data in R efficiently to prepare it for analysis
  • Master the skill of recognizing techniques for effective visualization of data
  • Understand why and how to create test and training data sets for analysis
  • Master fundamental learning methods such as linear and logistic regression
  • Comprehend advanced learning methods such as support vector machines
  • Learn how to use R in a cloud service such as Amazon

Authors

Table of Contents

Chapter 1: A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Chapter 2: Linear Regression - The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
Chapter 3: Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Discriminant analysis overview
Multivariate Adaptive Regression Splines (MARS)
Model selection
Summary
Chapter 4: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Regularization and classification
Summary
Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Business case
Feature selection for SVMs
Summary
Chapter 6: Classification and Regression Trees
An overview of the techniques
Business case
Summary
Chapter 7: Neural Networks and Deep Learning
Introduction to neural networks
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
Chapter 8: Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Random forest
Business understanding
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 9: Principal Components Analysis
An overview of the principal components
Business understanding
Modeling and evaluation
Summary
Chapter 10: Market Basket Analysis, Recommendation Engines, and Sequential Analysis
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
Business understanding and recommendations
Data understanding, preparation, and recommendations
Modeling, evaluation, and recommendations
Sequential data analysis
Summary
Chapter 11: Creating Ensembles and Multiclass Classification
Ensembles
Business and data understanding
Modeling evaluation and selection
Multiclass classification
Business and data understanding
Model evaluation and selection
MLR's ensemble
Summary
Chapter 12: Time Series and Causality
Univariate time series analysis
Business understanding
Modeling and evaluation
Summary
Chapter 13: Text Mining
Text mining framework and methods
Topic models
Business understanding
Modeling and evaluation
Summary
Chapter 14: R on the Cloud
Creating an Amazon Web Services account
Summary
Chapter 15: R Fundamentals
Getting R up-and-running
Using R
Data frames and matrices
Creating summary statistics
Installing and loading R packages
Data manipulation with dplyr
Summary
Chapter 16: Sources

Book Details

ISBN 139781787287471
Paperback420 pages
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