Mastering Machine Learning with R

Master machine learning techniques with R to deliver insights for complex projects
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Mastering Machine Learning with R

Cory Lesmeister

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

ISBN 139781783984527
Paperback400 pages

Book Description

Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data.

The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques.” Finally, the book will walk you through text analysis and time series.

The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems.By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.

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
Model selection
Summary
Chapter 4: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
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
Introduction
An overview of the techniques
Business case
Summary
Chapter 7: Neural Networks
Neural network
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
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 9: Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
Chapter 10: Market Basket Analysis and Recommendation Engines
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
Summary
Chapter 11: Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
Chapter 12: Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary

What You Will Learn

  • Gain deep insights to learn the applications of machine learning tools to 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
  • Familiarize yourself with fundamental learning methods such as linear and logistic regression
  • Comprehend advanced learning methods such as support vector machines
  • Realize why and how to apply unsupervised learning methods

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
Model selection
Summary
Chapter 4: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
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
Introduction
An overview of the techniques
Business case
Summary
Chapter 7: Neural Networks
Neural network
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
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 9: Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
Chapter 10: Market Basket Analysis and Recommendation Engines
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
Summary
Chapter 11: Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
Chapter 12: Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary

Book Details

ISBN 139781783984527
Paperback400 pages
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