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...

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Bibliographic Details
Main Author: Nwanganga, Fred.
Other Authors: Chapple, Mike.
Format: Electronic
Language: English
Published: Newark : John Wiley & Sons, Incorporated, 2020.
Subjects:
Machine learning.
R (Computer program language)
elektronické knihy
electronic books
ISBN: 9781119591573
1119591570
9781119591535
1119591538
1119591511
9781119591511
1523133198
9781523133192
1119591546
9781119591542
Physical Description: 1 online resource (466 p.)

Cover

Table of contents

LEADER 06105cam a2200541Mu 4500
001 kn-on1152054630
003 OCoLC
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006 m o d
007 cr cn|||||||||
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 
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 
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 
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 
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. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
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 Knovel (All titles) 
650 0 |a Machine learning. 
650 0 |a R (Computer program language) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Chapple, Mike. 
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://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpPMLR0005/practical-machine-learning?kpromoter=marc  |y Full text 

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