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Machine Learning with R - Fourth Edition
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Machine Learning with R - Fourth Edition

by Brett Lantz

Data Science

Book Details

Book Title

Machine Learning with R - Fourth Edition

Author

Brett Lantz

Publisher

Packt Publishing

Publication Date

2023

ISBN

9781801071321

Number of Pages

763

Language

English

Format

PDF

File Size

11MB

Subject

Machine Learning

Table of Contents

  • Machine Learning with R
  • Cover
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • 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 auto insurance claims costs 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
  • Finding teen market segments using k-means clustering
  • Summary
  • Chapter 10: Evaluating Model Performance
  • Measuring performance for classification
  • Estimating future performance
  • Summary
  • Chapter 11: Being Successful with Machine Learning
  • What makes a successful machine learning practitioner?
  • What makes a successful machine learning model?
  • Putting the “science” in data science
  • Summary
  • Chapter 12: Advanced Data Preparation
  • Performing feature engineering
  • Feature engineering in practice
  • Exploring R’s tidyverse
  • Summary
  • Chapter 13: Challenging Data – Too Much, Too Little, Too Complex
  • The challenge of high-dimension data
  • Making use of sparse data
  • Handling missing data
  • The problem of imbalanced data
  • Summary
  • Chapter 14: Building Better Learners
  • Tuning stock models for better performance
  • Improving model performance with ensembles
  • Stacking models for meta-learning
  • Summary
  • Chapter 15: Making Use of Big Data
  • Practical applications of deep learning
  • Unsupervised learning and big data
  • Adapting R to handle large datasets
  • Summary
  • Other Books You May Enjoy
  • Index