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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 |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2014.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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