最新章節(jié)

• Sources
• Summary
• Data manipulation with dplyr
• Installing and loading R packages
• Creating summary statistics
• Data frames and matrices

• 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