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Machine Learning with R - Second Edition

By : Brett Lantz
Book Image

Machine Learning with R - Second Edition

By: Brett Lantz

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Machine Learning with R - Second Edition
Brett Lantz
Jul, 2015 | 452 pages
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Table of Contents (19 chapters)
Machine Learning with R Second Edition
Machine Learning with R Second Edition
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introducing Machine Learning
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
2
Managing and Understanding Data
Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
3
Lazy Learning – Classification Using Nearest Neighbors
Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
4
Probabilistic Learning – Classification Using Naive Bayes
Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
5
Divide and Conquer – Classification Using Decision Trees and Rules
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
6
Forecasting Numeric Data – Regression Methods
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
7
Black Box Methods – Neural Networks and Support Vector Machines
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
8
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
9
Finding Groups of Data – Clustering with k-means
Finding Groups of Data – Clustering with k-means
Understanding clustering
Example – finding teen market segments using k-means clustering
Summary
10
Evaluating Model Performance
Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
11
Improving Model Performance
Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
12
Specialized Machine Learning Topics
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
Index
Index

About the Reviewers

Vijayakumar Nattamai Jawaharlal is a software engineer with an experience of 2 decades in the IT industry. His background lies in machine learning, big data technologies, business intelligence, and data warehouse.

He develops scalable solutions for many distributed platforms, and is very passionate about scalable distributed machine learning.

Kent S. Johnson is a software developer who loves data analysis, statistics, and machine learning. He currently develops software to analyze tissue samples related to cancer research. According to him, a day spent with R and ggplot2 is a good day. For more information about him, visit http://kentsjohnson.com.

I'd like to thank, Gile, for always loving me.

Mzabalazo Z. Ngwenya holds a postgraduate degree in mathematical statistics from the University of Cape Town. He has worked extensively in the field of statistical consulting, and currently works as a biometrician at a research and development entity in South Africa. His areas of interest are primarily centered around statistical computing, and he has over 10 years of experience with R for data analysis and statistical research. Previously, he was involved in reviewing Learning RStudio for R Statistical Computing, R Statistical Application Development by Example Beginner's Guide, R Graph Essentials, R Object-oriented Programming, Mastering Scientific Computing with R, and Machine Learning with R, all by Packt Publishing.

Anuj Saxena is a data scientist at IGATE Corporation. He has an MS in analytics from the University of San Francisco and an MSc in Statistics from the NMIMS University in India. He is passionate about data science and likes using open source languages such as R and Python as primary tools for data science projects. In his spare time, he participates in predictive analytics competitions on kaggle.com. For more information about him, visit http://www.anuj-saxena.com.

I'd like to thank my father, Dr. Sharad Kumar, who inspired me at an early age to learn math and statistics and my mother, Mrs. Ranjana Saxena, who has been a backbone throughout my educational life.

I'd also like to thank my wonderful professors at the University of San Francisco and the NMIMS University who triggered my interest in this field and taught me the power of data and how it can be used to tell a wonderful story.

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