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R machine learning essentials : gain quick access to the machine learning concepts and practical applications using the R development environment /
If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2014.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Usuelli, Michele, |e author. | |
| 245 | 1 | 0 | |a R machine learning essentials : |b gain quick access to the machine learning concepts and practical applications using the R development environment / |c Michele Usuelli. |
| 246 | 3 | 0 | |a Gain quick access to the machine learning concepts and practical applications using the R development environment |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2014. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from cover page (Safari, viewed January 5, 2015). | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Transforming Data into Actions; A data-driven approach in business decisions; Business decisions come from knowledge and expertise; The digital era provides more data and expertise; Technology connects data and businesses; Identifying hidden patterns; Data contains hidden information; Business problems require hidden information; Reshaping the data; Identifying patterns with unsupervised learning; Making business decisions with unsupervised learning | |
| 505 | 8 | |a Estimating the impact of an actionBusiness problems require estimating future events; Gathering the data to learn from; Predicting future outcomes using supervised learning; Summary; Chapter 2: R -- a Powerful Tool for Developing Machine Learning Algorithms; Why R; An interactive approach to machine learning; Expectations of machine learning software; R and RStudio; The R tutorial; The basic tools of R; Understanding the basic R objects; What are the R standards?; Some useful R packages; Summary; Chapter 3: A Simple Machine Learning Analysis; Exploring data interactively | |
| 505 | 8 | |a Defining a table with the dataVisualizing the data through a histogram; Visualizing the impact of a feature; Visualizing the impact of two features combined; Exploring the data using machine learning models; Exploring the data using a decision tree; Predicting newer outcomes; Building a machine learning model; Using the model to predict new outcomes; Validating a model; Summary; Chapter 4: Step1 -- Data Exploration and Feature Engineering; Building a machine learning solution; Building the feature data; Exploring and visualizing the features; Modifying the features | |
| 505 | 8 | |a Ranking the features using a filter or a dimensionality reductionSummary; Chapter 5: Step 2 -- Applying Machine Learning Techniques; Identifying homogeneous group of items; Identifying the groups using k-means; Exploring the clusters; Identifying a cluster's hierarchy; Applying the k-nearest-neighbour algorithm; Optimizing the k-nearest neighbour algorithm; Summary; Chapter 6: Step 3 -- Validating the Results; Validating a machine learning model; Measuring the accuracy of an algorithm; Defining the average accuracy; Visualizing the average accuracy computation; Tuning the parameters | |
| 505 | 8 | |a Selecting the data features to include in the modelTuning features and parameters together; Summary; Chapter 7: Overview of Machine Learning Techniques; Overview; Supervised learning; The k-nearest neighbors algorithm; Decision tree learning; Linear regression; Perceptron; Ensembles; Unsupervised learning; K-means; Hierarchical clustering; PCA; Summary; Chapter 8: Machine Learning Examples Applicable to Businesses; Overview of the problem; Data overview; Exploring the output; Exploring and transforming features; Clustering the clients; Predicting the output; Summary; Index | |
| 520 | |a If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
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| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
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| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
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