Practical machine learning in R (Record no. 68396)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06611cam a2200493Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | on1151188553 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220711203158.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200418s2020 enk o 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119591542 |
| -- | (electronic bk. : oBook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119591546 |
| -- | (electronic bk. : oBook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119591573 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119591570 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3/1 |
| 100 1# - AUTHOR NAME | |
| Author | Nwanganga, Frederick Chukwuka. |
| 245 10 - TITLE STATEMENT | |
| Title | Practical machine learning in R |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | London : |
| Publisher | ISTE, Ltd. ; |
| Place of publication | Hoboken : |
| Publisher | Wiley, |
| Year of publication | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (466 p.) |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Description based upon print version of record. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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 ## - GENERAL NOTE | |
| Remark 1 | Binomial Logistic Regression Model |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Guides 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Chapple, Mike, |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1002/9781119591542 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | R (Computer program language) |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | COMPUTERS / Software Development & Engineering / General. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| -- | (OCoLC)fst01004795 |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | R (Computer program language) |
| -- | (OCoLC)fst01086207 |
| 994 ## - | |
| -- | 92 |
| -- | DG1 |
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