Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5
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Language
English
ISBN
1838647058, 9781838647056
Notes
Description
This book is ideal for people wanting to get up-and-running with the core concepts of machine learning using R 3.5. This book follows a step-by-step approach to implementing an end-to-end pipeline, addressing data collection and processing, various types of data analysis, and machine learning use cases.
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O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Pastor Sanz, I. (2019). Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5 . Packt Publishing.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Pastor Sanz, Iván. 2019. Machine Learning With R Quick Start Guide: A Beginner's Guide to Implementing Machine Learning Techniques From Scratch Using R 3.5. Birmingham, UK: Packt Publishing.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Pastor Sanz, Iván. Machine Learning With R Quick Start Guide: A Beginner's Guide to Implementing Machine Learning Techniques From Scratch Using R 3.5 Birmingham, UK: Packt Publishing, 2019.
Harvard Citation (style guide)Pastor Sanz, I. (2019). Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5. Birmingham, UK: Packt Publishing.
MLA Citation, 9th Edition (style guide)Pastor Sanz, Iván. Machine Learning With R Quick Start Guide: A Beginner's Guide to Implementing Machine Learning Techniques From Scratch Using R 3.5 Packt Publishing, 2019.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
2eb3b01d-cedd-364f-234b-88b487ff8487-eng
Grouping Information
| Grouped Work ID | 2eb3b01d-cedd-364f-234b-88b487ff8487-eng |
|---|---|
| Full title | machine learning with r quick start guide a beginners guide to implementing machine learning techniques from scratch using r 3 5 |
| Author | pastor sanz iván |
| Grouping Category | book |
| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-05-22 03:09:18AM |
Book Cover Information
| Image Source | syndetics |
|---|---|
| First Loaded | Nov 3, 2024 |
| Last Used | Feb 12, 2025 |
Marc Record
| First Detected | Mar 21, 2023 12:10:48 PM |
|---|---|
| Last File Modification Time | Dec 17, 2024 08:09:10 AM |
| Suppressed | Record had no items |
MARC Record
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| 245 | 1 | 0 | |a Machine learning with R quick start guide :|b a beginner's guide to implementing machine learning techniques from scratch using R 3.5 /|c Iván Pastor Sanz. |
| 264 | 1 | |a Birmingham, UK :|b Packt Publishing,|c 2019. | |
| 300 | |a 1 online resource :|b illustrations | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: R Fundamentals for Machine Learning; R and RStudio installation; Things to know about R; Using RStudio; RStudio installation ; Some basic commands; Objects, special cases, and basic operators in R; Working with objects; Working with vectors; Vector indexing; Functions on vectors; Factor; Factor levels; Strings; String functions; Matrices; Representing matrices; Creating matrices; Accessing elements in a matrix; Matrix functions; Lists; Creating lists | |
| 505 | 8 | |a Accessing components and elements in a listData frames; Accessing elements in data frames; Functions of data frames; Importing or exporting data; Working with functions; Controlling code flow; All about R packages; Installing packages; Necessary packages; Taking further steps; Background on the financial crisis; Summary; Chapter 2: Predicting Failures of Banks -- Data Collection; Collecting financial data; Why FDIC?; Listing files; Finding files; Combining results; Removing tables; Knowing your observations; Handling duplications; Operating our problem; Collecting the target variable | |
| 505 | 8 | |a Structuring dataSummary; Chapter 3: Predicting Failures of Banks -- Descriptive Analysis; Data overview; Getting acquainted with our variables; Finding missing values for a variable; Converting the format of the variables; Sampling; Partitioning samples; Checking samples; Implementing descriptive analysis; Dealing with outliers; The winsorization process; Implementing winsorization; Distinguishing single valued variables; Treating missing information; Analyzing the missing value; Understanding the results; Summary; Chapter 4: Predicting Failures of Banks -- Univariate Analysis | |
| 505 | 8 | |a Feature selection algorithmFeature selection classes; Filter methods; Wrapper methods; Boruta package; Embedded methods; Ridge regression; A limitation of Ridge regression; Lasso ; Limitations of Lasso; Elastic net; Drawbacks of elastic net; Dimensionality reduction; Dimensionality reduction technique; Summary; Chapter 5: Predicting Failures of Banks -- Multivariate Analysis; Logistic regression; Regularized methods; Testing a random forest model; Gradient boosting; Deep learning in neural networks; Designing a neural network; Training a neural network; Support vector machines | |
| 505 | 8 | |a Selecting SVM parametersThe SVM kernel parameter; The cost parameter; Gamma parameter; Training an SVM model; Ensembles; Average model; Majority vote; Model of models; Automatic machine learning; Standardizing variables; Summary ; Chapter 6: Visualizing Economic Problems in the European Union; A general overview of economic problems in countries; Understanding credit ratings; The role of credit rating agencies; The credit rating process; Clustering countries based on macroeconomic imbalances; Data collection; Downloading and viewing the data; Streamlining data; Studying the data | |
| 520 | |a This book is ideal for people wanting to get up-and-running with the core concepts of machine learning using R 3.5. This book follows a step-by-step approach to implementing an end-to-end pipeline, addressing data collection and processing, various types of data analysis, and machine learning use cases. | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed May 9, 2019). | |
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