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The Statistics and Machine Learning with R Workshop

You're reading from   The Statistics and Machine Learning with R Workshop Unlock the power of efficient data science modeling with this hands-on guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Length 516 pages
Edition 1st Edition
Languages
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Author (1):
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Liu Peng Liu Peng
Author Profile Icon Liu Peng
Liu Peng
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Table of Contents (20) Chapters Close

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R FREE CHAPTER 3. Chapter 2: Data Processing with dplyr 4. Chapter 3: Intermediate Data Processing 5. Chapter 4: Data Visualization with ggplot2 6. Chapter 5: Exploratory Data Analysis 7. Chapter 6: Effective Reporting with R Markdown 8. Part 2:Fundamentals of Linear Algebra and Calculus in R
9. Chapter 7: Linear Algebra in R 10. Chapter 8: Intermediate Linear Algebra in R 11. Chapter 9: Calculus in R 12. Part 3:Fundamentals of Mathematical Statistics in R
13. Chapter 10: Probability Basics 14. Chapter 11: Statistical Estimation 15. Chapter 12: Linear Regression in R 16. Chapter 13: Logistic Regression in R 17. Chapter 14: Bayesian Statistics 18. Index 19. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

abline 115

accuracy rate 438

adjusted R2 394

aesthetics layer 101

analysis of variance (ANOVA) 336, 371, 384, 385

antiderivative 281, 296, 297

Area Under the Curve (AUC) 274, 438

arithmetic operations, R 9, 10

arrange() function 41

used, for sorting dataset 41-43

artificial intelligence (AI) 274

B

bar chart 119

building 124-127

Bayesian inference 465

normal-normal model 465

performing, with categorical variable 477-479

Bayesian linear regression

with categorical variable 477

Bayesian statistics 454

Bayesian theorem 454-456

Bernoulli-distributed random variables

analyzing 304-306

simulating 304-306

Bernoulli distribution 304

Bernoulli trial 302, 304

bigram representation 92

binary cross-entropy loss (CEL) 436

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