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Machine Learning with R

You're reading from   Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (18) Chapters Close

Preface 1. Introducing Machine Learning 2. Managing and Understanding Data FREE CHAPTER 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black-Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Being Successful with Machine Learning 12. Advanced Data Preparation 13. Challenging Data – Too Much, Too Little, Too Complex 14. Building Better Learners 15. Making Use of Big Data 16. Other Books You May Enjoy
17. Index

Preface

Machine learning, at its core, describes algorithms that transform data into actionable intelligence. This fact makes machine learning well suited to the present-day era of big data. Without machine learning, it would be nearly impossible to make sense of the massive streams of information that are now all around us.

The cross-platform, zero-cost statistical programming environment called R provides an ideal pathway to start applying machine learning. R offers powerful but easy-to-learn tools that can assist you with finding insights in your own data.

By combining hands-on case studies with the essential theory needed to understand how these algorithms work, this book delivers all the knowledge you need to get started with machine learning and to apply its methods to your own projects.

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