000 06611cam a2200493Ia 4500
001 on1151188553
003 OCoLC
005 20220711203158.0
006 m d
007 cr un|---aucuu
008 200418s2020 enk o 000 0 eng d
040 _aEBLCP
_beng
_cEBLCP
_dDG1
_dUKAHL
_dRECBK
_dOCLCF
020 _a9781119591542
_q(electronic bk. : oBook)
020 _a1119591546
_q(electronic bk. : oBook)
020 _a9781119591573
020 _a1119591570
035 _a(OCoLC)1151188553
050 4 _aQ325.5
082 0 4 _a006.3/1
_223
049 _aMAIN
100 1 _aNwanganga, Frederick Chukwuka.
_94990
245 1 0 _aPractical machine learning in R
_h[electronic resource] /
_cFred Nwanganga, Mike Chapple.
260 _aLondon :
_bISTE, Ltd. ;
_aHoboken :
_bWiley,
_c2020.
300 _a1 online resource (466 p.)
500 _aDescription based upon print version of 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.
650 0 _aMachine learning.
_91831
650 0 _aR (Computer program language)
_94991
650 7 _aCOMPUTERS / Software Development & Engineering / General.
_2bisacsh
_94992
650 7 _aMachine learning.
_2fast
_0(OCoLC)fst01004795
_91831
650 7 _aR (Computer program language)
_2fast
_0(OCoLC)fst01086207
_94991
655 4 _aElectronic books.
_93294
700 1 _aChapple, Mike,
_d1975-
_94993
776 0 8 _iPrint version:
_aNwanganga, Fred
_tPractical Machine Learning in R
_dNewark : John Wiley & Sons, Incorporated,c2020
_z9781119591511
856 4 0 _uhttps://doi.org/10.1002/9781119591542
_zWiley Online Library
942 _cEBK
994 _a92
_bDG1
999 _c68396
_d68396