Machine Learning with R - Second Edition

Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R

Machine Learning with R - Second Edition

Brett Lantz

3 customer reviews
Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
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Book Details

ISBN 139781784393908
Paperback452 pages

Book Description

Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.

With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.

Table of Contents

Chapter 1: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Chapter 2: Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
Chapter 6: Forecasting Numeric Data – Regression Methods
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example – performing OCR with SVMs
Summary
Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
Chapter 9: Finding Groups of Data – Clustering with k-means
Understanding clustering
Example – finding teen market segments using k-means clustering
Summary
Chapter 10: Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
Chapter 11: Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
Chapter 12: Specialized Machine Learning Topics
Working with proprietary files and databases
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary

What You Will Learn

  • Harness the power of R to build common machine learning algorithms with real-world data science applications
  • Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
  • Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
  • Classify your data with Bayesian and nearest neighbor methods
  • Predict values by using R to build decision trees, rules, and support vector machines
  • Forecast numeric values with linear regression, and model your data with neural networks
  • Evaluate and improve the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, big data, and more

Authors

Table of Contents

Chapter 1: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Chapter 2: Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
Chapter 6: Forecasting Numeric Data – Regression Methods
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example – performing OCR with SVMs
Summary
Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
Chapter 9: Finding Groups of Data – Clustering with k-means
Understanding clustering
Example – finding teen market segments using k-means clustering
Summary
Chapter 10: Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
Chapter 11: Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
Chapter 12: Specialized Machine Learning Topics
Working with proprietary files and databases
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary

Book Details

ISBN 139781784393908
Paperback452 pages
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From 3 reviews

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