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

Uses and abuses of machine learning

Most people have heard of Deep Blue, the chess-playing computer that in 1997 was the first to win a game against a world champion. Another famous computer, Watson, defeated two human opponents on the television trivia game show Jeopardy in 2011. Based on these stunning accomplishments, some have speculated that computer intelligence will replace workers in information technology occupations, just as automobiles replaced horses and machines replaced workers in fields and assembly lines. Recently, these fears have become more pronounced as artificial intelligence-based algorithms, such as GPT-3 and DALL·E 2 from the OpenAI research group (https://openai.com/), have reached impressive milestones and are proving that computers are capable of writing text and creating artwork that is virtually indistinguishable from that produced by humans. Ultimately, this may lead to massive shifts in occupations like marketing, customer support, illustration...

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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

Uses and abuses of machine learning

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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

Uses and abuses of machine learning

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