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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781801071321

Length 762 pages

Edition 4th Edition

Languages

Tools

H2O

Concepts

Big Data

Author (1):

Brett Lantz

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Table of Contents (18) Chapters

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

Putting the “science” in data science

In the time since the first edition of Machine Learning with R was published, a new phrase has become somewhat ubiquitous within the field of machine learning. That buzzword, of course, is data science—a term that has been defined by many but is generally agreed to describe a field of work or study encapsulating aspects of statistics, data preparation and visualization, subject-matter expertise, as well as machine learning.

It is debatable whether data science is synonymous with what used to be called data mining, but it is safe to assume that there is a lot of overlap between the two. A reasonable outsider might observe that data science is simply a more formalized version of data mining. The methods and techniques in data mining were often learned informally on the job or passed between practitioners at industry events. This is in stark contrast to the field of data science, which offers countless opportunities to earn...

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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

Table of Contents (18) Chapters

Putting the “science” in data science

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Table of Contents (18) Chapters

Putting the “science” in data science

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