000			07344cam a2200637 a 4500
001			on1151188553
003			OCoLC
005			20240523125542.0
006			m o d
007			cr un|---aucuu
008			200418s2020 enk o 000 0 eng d
040			_aEBLCP _beng _epn _cEBLCP _dDG1 _dUKAHL _dRECBK _dOCLCF _dUBY _dOCLCQ _dOCLCO _dOCLCQ _dOCLCO _dOCLCQ _dUPM _dOCLCQ _dOCLCO _dOCLCL _dDXU _dOCLCO
020			_a9781119591542 _q(electronic bk. ; _qoBook)
020			_a1119591546 _q(electronic bk. ; _qoBook)
020			_a9781119591573
020			_a1119591570
029	1		_aAU@ _b000067253883
035			_a(OCoLC)1151188553
050		4	_aQ325.5
082	0	4	_a006.3/1 _223
049			_aMAIN
100	1		_aNwanganga, Frederick Chukwuka.
245	1	0	_aPractical machine learning in R / _cFred Nwanganga, Mike Chapple.
260			_aLondon : _bISTE, Ltd. ; _aHoboken : _bWiley, _c2020.
300			_a1 online resource (466 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aPrint version record.
505	0		_aCover -- 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		_aModel 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		_aChapter 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		_aTransforming 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		_aInfluential 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			_aBinomial Logistic Regression Model
520			_aGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language 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.' -Explores data management techniques, including data collection, exploration and dimensionality reduction -Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering -Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques -Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.
590			_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks
650		0	_aMachine learning.
650		0	_aR (Computer program language)
650		6	_aApprentissage automatique.
650		6	_aR (Langage de programmation)
650		7	_aCOMPUTERS _xSoftware Development & Engineering _xGeneral. _2bisacsh
650		7	_aMachine learning _2fast
650		7	_aR (Computer program language) _2fast
650		7	_aAprenentatge autom�atic. _2thub
650		7	_aR (Llenguatge de programaci�o) _2thub
655		7	_aLlibres electr�onics. _2thub
700	1		_aChapple, Mike, _d1975-
758			_ihas work: _aPractical machine learning in R (Text) _1https://id.oclc.org/worldcat/entity/E39PCGXf4BdG3hGRr6kpvCRhH3 _4https://id.oclc.org/worldcat/ontology/hasWork
776	0	8	_iPrint version: _aNwanganga, Fred. _tPractical Machine Learning in R. _dNewark : John Wiley & Sons, Incorporated, �2020 _z9781119591511
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119591542
938			_aAskews and Holts Library Services _bASKH _nAH36662083
938			_aProQuest Ebook Central _bEBLB _nEBL6174019
938			_aRecorded Books, LLC _bRECE _nrbeEB00821158
994			_a92 _bINLUM
999			_c12706 _d12706