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|a UAMI
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|a Nwanganga, Fred.
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245 |
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|a Practical Machine Learning in R
|h [electronic resource].
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260 |
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|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2020.
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300 |
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|a 1 online resource (466 p.)
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|a text
|2 rdacontent
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|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
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505 |
8 |
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|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
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505 |
8 |
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|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
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|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
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505 |
8 |
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|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
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500 |
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|a Binomial Logistic Regression Model
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504 |
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|a Includes bibliographical references and index.
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520 |
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|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 |
|
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
|
590 |
|
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|a Knovel
|b ACADEMIC - General Engineering & Project Administration
|
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
|0 (OCoLC)fst01004795
|
650 |
|
7 |
|a R (Computer program language)
|2 fast
|0 (OCoLC)fst01086207
|
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
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