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

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.