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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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781801071321

Length 762 pages

Edition 4th Edition

Languages

R

Tools

H2O

Concepts

Big Data

Author (1):

Brett Lantz

View More author details

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

Index

Symbols

1-NN classification 89

1R (One Rule) algorithm 178-180

strengths 179

weakness 179

68-95-99.7 rule 72

A

abstraction 16

actionable rule 338

activation function 269

Gaussian activation function 272

linear activation function 272

sigmoid activation function 270

threshold activation function 269

unit step activation function 269

active after-marketing 322

actuarial science 218

AdaBoost (adaptive boosting) 616

AdaBoost.M1 algorithm 616

advanced data exploration

data exploration roadmap, constructing 461-463

ggplot2, for visual data exploration 467-480

outliers, encountering 464-466

adversarial learning 30

agglomerative clustering 352

allocation function 610

Amazon Mechanical Turk

URL 510

Amazon Web Services (AWS) 294, 698

antecedent 175

Apache Hadoop 706

Apache Spark 706

Apriori algorithm 317

Apriori principle

used, for building set of rules...

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Authors (1)

Brett Lantz

Brett Lantz

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

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