Practical Machine Learning in R

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"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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nwanganga, Fred
Otros Autores: Chapple, Mike
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2020.
Temas:
Machine learning.
R (Computer program language)
Apprentissage automatique.
R (Langage de programmation)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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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