Mastering machine learning with R : master machine learning techniques with R to deliver insights for complex projects / Cory Lesmeister.

Format:

Book

Author/Creator:

Lesmeister, Cory, author.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

R (Computer program language).

Machine learning.

Physical Description:

1 online resource (400 p.)

Place of Publication:

Birmingham : Packt Publishing, 2015.

Language Note:

English

Summary:

Master machine learning techniques with R to deliver insights for complex projectsAbout This Book* Get to grips with the application of Machine Learning methods using an extensive set of R packages* Understand the benefits and potential pitfalls of using machine learning methods* Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML systemWho This Book Is ForIf you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.What You Will Learn* Gain deep insights to learn the applications of machine learning tools to 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* Familiarize yourself with fundamental learning methods such as linear and logistic regression* Comprehend advanced learning methods such as support vector machines* Realize why and how to apply unsupervised learning methodsIn DetailMachine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R--a cross-platform, zero-cost statistical programming environment--there has never been a better time to start applying machine learning to your data.The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of "Unsupervised techniques". Finally, the book will walk you through text analysis and time series.The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.Style and approachThis is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

Contents:

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: A Process for Success; The process; Business understanding; Identify the business objective; Assess the situation; Determine the analytical goals; Produce 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

Data understanding and preparationModeling and evaluation; Other linear model considerations; Qualitative feature; Interaction term; 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; Model selection; Summary; Chapter 4: Advanced Feature Selection in Linear Models

Regularization in a nutshellRidge 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; Summary; Chapter 5: More Classification Techniques - K-Nearest Neighbors and Support Vector Machines; K-Nearest Neighbors; Support Vector Machines; Business case; Business understanding; Data understanding and preparation; Modeling and evaluation; KNN modeling; SVM modeling; Model selection; Feature selection for SVMs; Summary

Chapter 6: Classification and Regression TreesIntroduction; An overview of the techniques; Regression trees; Classification trees; Random forest; Gradient boosting; Business case; Modeling and evaluation; Regression Tree; Classification tree; Random forest regression; Random forest classification; Gradient boosting regression; Gradient boosting classification; Model selection; Summary; Chapter 7: Neural Networks; Neural network; Deep learning, a not-so-deep overview; Business understanding; Data understanding and preparation; Modeling and evaluation; An example of deep learning

H2O backgroundData preparation and uploading it 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; Business understanding; Data understanding and preparation; Modeling and evaluation; Hierarchical clustering; K-means clustering; Clustering with mixed data; Summary; Chapter 9: Principal Components Analysis; An overview of the principal components; Rotation; Business understanding; Data understanding and preparation; Modeling and evaluation

Component extraction

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed January 4, 2016).

ISBN:

9781783984534

1783984538