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Mastering Machine Learning with R - Second Edition.
Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Cl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Lesmeister, Cory. | |
| 245 | 1 | 0 | |a Mastering Machine Learning with R - Second Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (410 pages) | ||
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| 505 | 0 | |a Cover; Credits; About the Author; About the Reviewers; Packt Upsell; Customer Feedback; Table of Contents; Preface; Chapter 1: A Process for Success; The process; Business understanding; Identifying the business objective; Assessing the situation; Determining the analytical goals; Producing a project plan; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Algorithm flowchart; Summary; Chapter 2: Linear Regression -- The Blocking and Tackling of Machine Learning; Univariate linear regression; Business understanding; Multivariate linear regression; Business understanding. | |
| 505 | 8 | |a Data understanding and preparationModeling and evaluation; Other linear model considerations; Qualitative features; Interaction terms; Summary; Chapter 3: Logistic Regression and Discriminant Analysis; Classification methods and linear regression; Logistic regression; Business understanding; Data understanding and preparation; Modeling and evaluation; The logistic regression model; Logistic regression with cross-validation; Discriminant analysis overview; Discriminant analysis application; Multivariate Adaptive Regression Splines (MARS); Model selection; Summary. | |
| 505 | 8 | |a Chapter 4: Advanced Feature Selection in Linear ModelsRegularization in a nutshell; Ridge regression; LASSO; Elastic net; Business case; Business understanding; Data understanding and preparation; Modeling and evaluation; Best subsets; Ridge regression; LASSO; Elastic net; Cross-validation with glmnet; Model selection; Regularization and classification; Logistic regression example ; Summary; Chapter 5: More Classification Techniques -- K-Nearest Neighbors and Support Vector Machines; K-nearest neighbors; Support vector machines; Business case; Business understanding. | |
| 505 | 8 | |a Data understanding and preparationModeling and evaluation; KNN modeling; SVM modeling; Model selection; Feature selection for SVMs; Summary; Chapter 6: Classification and Regression Trees; An overview of the techniques; Understanding the regression trees; Classification trees; Random forest; Gradient boosting; Business case; Modeling and evaluation; Regression tree; Classification tree; Random forest regression; Random forest classification; Extreme gradient boosting -- classification; Model selection; Feature Selection with random forests; Summary; Chapter 7: Neural Networks and Deep Learning. | |
| 505 | 8 | |a Introduction to neural networksDeep learning, a not-so-deep overview; Deep learning resources and advanced methods; Business understanding; Data understanding and preparation; Modeling and evaluation; An example of deep learning; H2O background; Data upload to H2O; Create train and test datasets; Modeling; Summary; Chapter 8: Cluster Analysis; Hierarchical clustering; Distance calculations; K-means clustering; Gower and partitioning around medoids; Gower; PAM; Random forest; Business understanding; Data understanding and preparation; Modeling and evaluation; Hierarchical clustering. | |
| 500 | |a K-means clustering. | ||
| 520 | |a Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn Gain deep insights into the application of machine learning tools in the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Master fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages. Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If ... | ||
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| 776 | 0 | 8 | |i Print version: |a Lesmeister, Cory. |t Mastering Machine Learning with R - Second Edition. |d Birmingham : Packt Publishing, ©2017 |
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