Cargando…
Practical Machine Learning in R
"Machine learning--a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions--allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Pra...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
| LEADER | 00000cam a2200000Mu 4500 | ||
|---|---|---|---|
| 001 | EBOOKCENTRAL_on1152054630 | ||
| 003 | OCoLC | ||
| 005 | 20240329122006.0 | ||
| 006 | m d | ||
| 007 | cr ||||||||||| | ||
| 008 | 200502s2020 xx o ||| 0 eng d | ||
| 040 | |a EBLCP |b eng |c EBLCP |d YDX |d UKMGB |d OCLCF |d UAB |d DKU |d VT2 |d SOE |d OCLCO |d OCLCQ |d OCLCO |d OCLCL |d HF9 | ||
| 015 | |a GBC037035 |2 bnb | ||
| 016 | 7 | |a 019738702 |2 Uk | |
| 019 | |a 1150921697 |a 1150942988 |a 1164363482 |a 1264917582 |a 1281712093 |a 1287267135 |a 1287876520 | ||
| 020 | |a 9781119591573 | ||
| 020 | |a 1119591570 | ||
| 020 | |a 9781119591535 |q (electronic bk.) | ||
| 020 | |a 1119591538 |q (electronic bk.) | ||
| 020 | |z 1119591511 | ||
| 020 | |z 9781119591511 | ||
| 020 | |a 1523133198 | ||
| 020 | |a 9781523133192 | ||
| 020 | |a 1119591546 | ||
| 020 | |a 9781119591542 | ||
| 029 | 1 | |a UKMGB |b 019738702 | |
| 029 | 1 | |a AU@ |b 000069768261 | |
| 035 | |a (OCoLC)1152054630 |z (OCoLC)1150921697 |z (OCoLC)1150942988 |z (OCoLC)1164363482 |z (OCoLC)1264917582 |z (OCoLC)1281712093 |z (OCoLC)1287267135 |z (OCoLC)1287876520 | ||
| 037 | |a 9781119591535 |b Wiley | ||
| 050 | 4 | |a Q320.5 |b .N836 2020 | |
| 082 | 0 | 4 | |a 006.31 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Nwanganga, Fred. | |
| 245 | 1 | 0 | |a Practical Machine Learning in R |h [electronic resource]. |
| 260 | |a Newark : |b John Wiley & Sons, Incorporated, |c 2020. | ||
| 300 | |a 1 online resource (466 p.) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 347 | |a text file | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright Page -- About the Authors -- About the Technical Editors -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- What Does This Book Cover? -- Reader Support for This Book -- Part I Getting Started -- Chapter 1 What Is Machine Learning? -- Discovering Knowledge in Data -- Introducing Algorithms -- Artificial Intelligence, Machine Learning, and Deep Learning -- Machine Learning Techniques -- Supervised Learning -- Unsupervised Learning -- Model Selection -- Classification Techniques -- Regression Techniques -- Similarity Learning Techniques | |
| 505 | 8 | |a Model Evaluation -- Classification Errors -- Regression Errors -- Types of Error -- Partitioning Datasets -- Holdout Method -- Cross-Validation Methods -- Exercises -- Chapter 2 Introduction to R and RStudio -- Welcome to R -- R and RStudio Components -- The R Language -- RStudio -- RStudio Desktop -- RStudio Server -- Exploring the RStudio Environment -- R Packages -- The CRAN Repository -- Installing Packages -- Loading Packages -- Package Documentation -- Writing and Running an R Script -- Data Types in R -- Vectors -- Testing Data Types -- Converting Data Types -- Missing Values -- Exercises | |
| 505 | 8 | |a Chapter 3 Managing Data -- The Tidyverse -- Data Collection -- Key Considerations -- Collecting Ground Truth Data -- Data Relevance -- Quantity of Data -- Ethics -- Importing the Data -- Reading Comma-Delimited Files -- Reading Other Delimited Files -- Data Exploration -- Describing the Data -- Instance -- Feature -- Dimensionality -- Sparsity and Density -- Resolution -- Descriptive Statistics -- Visualizing the Data -- Comparison -- Relationship -- Distribution -- Composition -- Data Preparation -- Cleaning the Data -- Missing Values -- Noise -- Outliers -- Class Imbalance | |
| 505 | 8 | |a Transforming the Data -- Normalization -- Discretization -- Dummy Coding -- Reducing the Data -- Sampling -- Dimensionality Reduction -- Exercises -- Part II Regression -- Chapter 4 Linear Regression -- Bicycle Rentals and Regression -- Relationships Between Variables -- Correlation -- Regression -- Simple Linear Regression -- Ordinary Least Squares Method -- Simple Linear Regression Model -- Evaluating the Model -- Residuals -- Coefficients -- Diagnostics -- Multiple Linear Regression -- The Multiple Linear Regression Model -- Evaluating the Model -- Residual Diagnostics | |
| 505 | 8 | |a Influential Point Analysis -- Multicollinearity -- Improving the Model -- Considering Nonlinear Relationships -- Considering Categorical Variables -- Considering Interactions Between Variables -- Selecting the Important Variables -- Strengths and Weaknesses -- Case Study: Predicting Blood Pressure -- Importing the Data -- Exploring the Data -- Fitting the Simple Linear Regression Model -- Fitting the Multiple Linear Regression Model -- Exercises -- Chapter 5 Logistic Regression -- Prospecting for Potential Donors -- Classification -- Logistic Regression -- Odds Ratio | |
| 500 | |a Binomial Logistic Regression Model | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Machine learning--a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions--allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more"--Amazon. | ||
| 590 | |a Knovel |b ACADEMIC - General Engineering & Project Administration | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Chapple, Mike. | |
| 758 | |i has work: |a Practical machine learning in R (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGXf4BdG3hGRr6kpvCRhH3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Nwanganga, Fred |t Practical Machine Learning in R |d Newark : John Wiley & Sons, Incorporated,c2020 |z 9781119591511 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781119591511/?ar |z Texto completo |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6174019 |z Texto completo |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6174019 | ||
| 938 | |a YBP Library Services |b YANK |n 301217273 | ||
| 938 | |a YBP Library Services |b YANK |n 16446282 | ||
| 938 | |a YBP Library Services |b YANK |n 16729622 | ||
| 994 | |a 92 |b IZTAP | ||