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Machine Learning with R

Progressing
Brett Lantz

R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning.
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Book Details

ISBN 139781782162148
Paperback396 pages

About This Book

  • Harness the power of R for statistical computing and data science
  • Use R to apply common machine learning algorithms with real-world applications
  • Prepare, examine, and visualize data for analysis
  • Understand how to choose between machine learning models
  • Packed with clear instructions to explore, forecast, and classify data

Who This Book Is For

Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Table of Contents

Chapter 1: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How do machines learn?
Steps to apply machine learning to your data
Choosing a machine learning algorithm
Using R for machine learning
Summary
Chapter 2: Managing and Understanding Data
R data structures
Vectors
Factors
Managing data with R
Exploring and understanding data
Summary
Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
Understanding classification using nearest neighbors
Diagnosing breast cancer with the kNN 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
Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
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
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 specialized data
Improving the performance of R
Summary

What You Will Learn

  • Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
  • Use R to prepare data for machine learning
  • Explore and visualize data with R
  • Classify data using nearest neighbor methods
  • Learn about Bayesian methods for classifying data
  • Predict values using decision trees, rules, and support vector machines
  • Forecast numeric values using linear regression
  • Model data using neural networks
  • Find patterns in data using association rules for market basket analysis
  • Group data into clusters for segmentation
  • Evaluate and improve the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, and “big” data

In Detail

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". 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. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.

"Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.

How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

"Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.

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