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Mastering Machine Learning with R（Second Edition）
Cory Lesmeister 著
更新時間：2021-07-09 18:24:30
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Thisbookisfordatascienceprofessionals,dataanalysts,oranyonewithaworkingknowledgeofmachinelearning,withRwhonowwanttotaketheirskillstothenextlevelandbecomeanexpertinthefield.
最新章節(jié)
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- Summary
- Data manipulation with dplyr
- Installing and loading R packages
- Creating summary statistics
- Data frames and matrices
品牌：中圖公司
上架時間：2021-07-09 18:04:31
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Sources 更新時間：2021-07-09 18:24:30
- Summary
- Data manipulation with dplyr
- Installing and loading R packages
- Creating summary statistics
- Data frames and matrices
- Using R
- Getting R up-and-running
- R Fundamentals
- Summary
- Start RStudio
- Launch a virtual machine
- Creating an Amazon Web Services account
- R on the Cloud
- Summary
- Additional quantitative analysis
- Word frequency and topic models
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Other quantitative analyses
- Topic models
- Text mining framework and methods
- Text Mining
- Summary
- Vector autoregression
- Linear regression
- Examining the causality
- Univariate time series forecasting
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Understanding Granger causality
- Univariate time series analysis
- Time Series and Causality
- Summary
- MLR's ensemble
- Ridge regression
- Random forest
- Model evaluation and selection
- Business and data understanding
- Multiclass classification
- Modeling evaluation and selection
- Business and data understanding
- Ensembles
- Creating Ensembles and Multiclass Classification
- Summary
- Sequential analysis applied
- Sequential data analysis
- Modeling evaluation and recommendations
- Data understanding preparation and recommendations
- Business understanding and recommendations
- Singular value decomposition and principal components analysis
- Item-based collaborative filtering
- User-based collaborative filtering
- An overview of a recommendation engine
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- An overview of a market basket analysis
- Market Basket Analysis Recommendation Engines and Sequential Analysis
- Summary
- Regression analysis
- Creating factor scores from the components
- Orthogonal rotation and interpretation
- Component extraction
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Rotation
- An overview of the principal components
- Principal Components Analysis
- Summary
- Random Forest and PAM
- Gower and PAM
- K-means clustering
- Hierarchical clustering
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Random forest
- PAM
- Gower
- Gower and partitioning around medoids
- K-means clustering
- Distance calculations
- Hierarchical clustering
- Cluster Analysis
- Summary
- Modeling
- Create train and test datasets
- Data upload to H2O
- H2O background
- An example of deep learning
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Deep learning resources and advanced methods
- Deep learning a not-so-deep overview
- Introduction to neural networks
- Neural Networks and Deep Learning
- Summary
- Feature Selection with random forests
- Model selection
- Extreme gradient boosting - classification
- Random forest classification
- Random forest regression
- Classification tree
- Regression tree
- Modeling and evaluation
- Business case
- Gradient boosting
- Random forest
- Classification trees
- Understanding the regression trees
- An overview of the techniques
- Classification and Regression Trees
- Summary
- Feature selection for SVMs
- Model selection
- SVM modeling
- KNN modeling
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Business case
- Support vector machines
- K-nearest neighbors
- More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
- Summary
- Logistic regression example
- Regularization and classification
- Model selection
- Cross-validation with glmnet
- Elastic net
- LASSO
- Ridge regression
- Best subsets
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Business case
- Elastic net
- LASSO
- Ridge regression
- Regularization in a nutshell
- Advanced Feature Selection in Linear Models
- Summary
- Model selection
- Multivariate Adaptive Regression Splines (MARS)
- Discriminant analysis application
- Discriminant analysis overview
- Logistic regression with cross-validation
- The logistic regression model
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Logistic regression
- Classification methods and linear regression
- Logistic Regression and Discriminant Analysis
- Summary
- Interaction terms
- Qualitative features
- Other linear model considerations
- Modeling and evaluation
- Data understanding and preparation
- Business understanding
- Multivariate linear regression
- Business understanding
- Univariate linear regression
- Linear Regression - The Blocking and Tackling of Machine Learning
- Summary
- Algorithm flowchart
- Deployment
- Evaluation
- Modeling
- Data preparation
- Data understanding
- Producing a project plan
- Determining the analytical goals
- Assessing the situation
- Identifying the business objective
- Business understanding
- The process
- A Process for Success
- Questions
- Piracy
- Errata
- Downloading the color images of this book
- Downloading the example code
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Customer Feedback
- Packt Upsell
- About the Reviewers
- About the Author
- Credits
- 版權(quán)信息
- 封面
- 封面
- 版權(quán)信息
- Credits
- About the Author
- About the Reviewers
- Packt Upsell
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- 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
- Linear Regression - The Blocking and Tackling of Machine Learning
- Univariate linear regression
- Business understanding
- Multivariate linear regression
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- Other linear model considerations
- Qualitative features
- Interaction terms
- Summary
- 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
- Advanced Feature Selection in Linear Models
- Regularization 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
- 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
- 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
- Neural Networks and Deep Learning
- Introduction to neural networks
- Deep 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
- 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
- K-means clustering
- Gower and PAM
- Random Forest and PAM
- Summary
- Principal Components Analysis
- An overview of the principal components
- Rotation
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- Component extraction
- Orthogonal rotation and interpretation
- Creating factor scores from the components
- Regression analysis
- Summary
- Market Basket Analysis Recommendation Engines and Sequential Analysis
- An overview of a market basket analysis
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- An overview of a recommendation engine
- User-based collaborative filtering
- Item-based collaborative filtering
- Singular value decomposition and principal components analysis
- Business understanding and recommendations
- Data understanding preparation and recommendations
- Modeling evaluation and recommendations
- Sequential data analysis
- Sequential analysis applied
- Summary
- Creating Ensembles and Multiclass Classification
- Ensembles
- Business and data understanding
- Modeling evaluation and selection
- Multiclass classification
- Business and data understanding
- Model evaluation and selection
- Random forest
- Ridge regression
- MLR's ensemble
- Summary
- Time Series and Causality
- Univariate time series analysis
- Understanding Granger causality
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- Univariate time series forecasting
- Examining the causality
- Linear regression
- Vector autoregression
- Summary
- Text Mining
- Text mining framework and methods
- Topic models
- Other quantitative analyses
- Business understanding
- Data understanding and preparation
- Modeling and evaluation
- Word frequency and topic models
- Additional quantitative analysis
- Summary
- R on the Cloud
- Creating an Amazon Web Services account
- Launch a virtual machine
- Start RStudio
- Summary
- R Fundamentals
- Getting R up-and-running
- Using R
- Data frames and matrices
- Creating summary statistics
- Installing and loading R packages
- Data manipulation with dplyr
- Summary
- Sources 更新時間：2021-07-09 18:24:30
