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Advanced Machine Learning with R
Cory Lesmeister Dr. Sunil Kumar Chinnamgari 著
更新時(shí)間：2021-06-24 14:25:35
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Risoneofthemostpopularlanguageswhenitcomestoexploringthemathematicalsideofmachinelearningandeasilyperformingcomputationalstatistics.ThisLearningPathshowsyouhowtoleveragetheRecosystemtobuildefficientmachinelearningapplicationsthatcarryoutintelligenttaskswithinyourorganization.You'lltacklerealisticprojectssuchasbuildingpowerfulmachinelearningmodelswithensemblestopredictemployeeattrition.You'llexploredifferentclusteringtechniquestosegmentcustomersusingwholesaledataanduseTensorFlowandKeras-Rforperformingadvancedcomputations.You’llalsobeintroducedtoreinforcementlearningalongwithitsvarioususecasesandmodels.Additionally,itshowsyouhowsomeoftheseblack-boxmodelscanbediagnosedandunderstood.BytheendofthisLearningPath,you’llbeequippedwiththeskillsyouneedtodeploymachinelearningtechniquesinyourownprojects.ThisLearningPathincludescontentfromthefollowingPacktproducts:RMachineLearningProjectsbyDr.SunilKumarChinnamgari.MasteringMachineLearningwithR-ThirdEditionbyCoryLesmeister.
最新章節(jié)
- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- Summary
- Creating a new package
- Creating a Package
- Summary
品牌：中圖公司
上架時(shí)間：2021-06-24 12:18:33
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
- Leave a review - let other readers know what you think 更新時(shí)間：2021-06-24 14:25:35
- Other Books You May Enjoy
- Summary
- Creating a new package
- Creating a Package
- Summary
- Solving the MABP with UCB and Thompson sampling algorithms
- Multi-arm bandit – real-world use cases
- Thompson sampling
- The upper confidence bound algorithm
- Decayed epsilon greedy
- Boltzmann or softmax exploration
- The epsilon-greedy algorithm
- Strategies for solving MABP
- The multi-arm bandit problem
- Terminology of RL
- Comparison of RL with other ML algorithms
- Understanding RL
- Winning the Casino Slot Machines with Reinforcement Learning
- Summary
- Implementing the project
- Building an automated prose generator with an RNN
- Vanishing gradients
- Exploding gradients
- Problems and solutions to gradients in RNN
- Backpropagation through time
- Comparison of feedforward neural networks and RNNs
- Exploring recurrent neural networks
- Understanding language models
- Automatic Prose Generation with Recurrent Neural Networks
- Summary
- Autoencoder code implementation for credit card fraud detection
- Building AEs with the H2O library in R
- The credit card fraud dataset
- Applications of AEs
- Types of AEs based on restrictions
- Types of AEs based on hidden layers
- Autoencoders explained
- Machine learning in credit card fraud detection
- Credit Card Fraud Detection Using Autoencoders
- Summary
- Implementing computer vision with pretrained models
- Implementing the LeNet architecture with the MXNet library
- Implementing dropout to avoid overfitting
- Implementing a deep learning network for handwritten digit recognition
- Understanding the MNIST dataset
- Introduction to the MXNet framework
- Layers of CNNs
- Convolutional Neural Networks
- Achieving computer vision with deep learning
- Understanding computer vision
- Technical requirements
- Image Recognition Using Deep Neural Networks
- Summary
- Identifying the customer segments in the wholesale customers data using AGNES
- Identifying the customer segments in the wholesale customer data using DIANA
- Working mechanics of the k-means algorithm
- Identifying the customer segments in wholesale customer data using k-means clustering
- Categories of clustering algorithms
- Understanding the wholesale customer dataset and the segmentation problem
- Understanding customer segmentation
- Customer Segmentation Using Wholesale Data
- Summary
- Building a text sentiment classifier with fastText
- Building a text sentiment classifier with GloVe word embedding
- Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
- Understanding word embedding
- Pros and cons of the BoW approach
- Building a text sentiment classifier with the BoW approach
- Understanding the Amazon reviews dataset
- Getting started
- The sentiment analysis problem
- Sentiment Analysis of Amazon Reviews with NLP
- References
- Summary
- Building a hybrid recommendation system for Jokes recommendations
- Differentiating between ITCF and content-based recommendations
- Content-based recommendation engine
- The Apriori algorithm
- Building a recommendation system based on an association-rule mining technique
- Building a recommendation system with a user-based collaborative filtering technique
- Building a recommendation system with an item-based collaborative filtering technique
- Dividing the DataFrame
- Converting the DataFrame
- Understanding the Jokes recommendation problem and the dataset
- Getting started
- Hybrid filtering
- Collaborative filtering
- Content-based filtering
- Recommendation engine categories
- Fundamental aspects of recommendation engines
- Implementing a Jokes Recommendation Engine
- Summary
- Building attrition prediction model with stacking
- Stacking
- Building attrition prediction model with XGBoost
- The GBM implementation
- Boosting
- Implementing an attrition prediction model with random forests
- Randomization with random forests
- Naive Bayes (nbBag) bagging implementation
- Support vector machine bagging (SVMBag) implementation
- Bagged classification and regression trees (treeBag) implementation
- Bagging
- K-nearest neighbors model for benchmarking the performance
- Understanding the attrition problem and the dataset
- Getting started
- Philosophy behind ensembling
- Predicting Employee Attrition Using Ensemble Models
- Summary
- Datasets
- Learning paradigm
- Model deployment
- Model building and evaluation
- Preparing the data
- Understanding and sourcing the data
- Business understanding
- ML project pipeline
- Model interpretability
- Feature engineering
- Performance metrics
- Hyperparameter tuning
- Holdout sample
- Data preprocessing
- Underfitting and overfitting
- Model bias and variance
- Class imbalance problem
- Dimensionality reduction
- Response variable
- Predictor variables
- Confusion matrix
- Model accuracy
- Cost function
- Computer vision
- Natural language processing
- Big data
- Deep learning
- ML terminology – a quick review
- Transfer learning
- Reinforcement learning
- Semi-supervised learning
- Unsupervised learning
- Supervised learning
- Types of ML methods
- ML versus software engineering
- Exploring the Machine Learning Landscape
- Summary
- Additional quantitative analysis
- LASSO model
- Data preparation
- Classifying text
- Topic models
- N-grams
- Sentiment analysis
- Lincoln's word frequency
- Word frequency in all addresses
- Word frequency
- Data frame creation
- Data overview
- Other quantitative analysis
- 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 exploration
- Time series data
- Understanding Granger causality
- Univariate time series analysis
- Time Series and Causality
- Summary
- Modeling and evaluation
- Data preparation
- Data understanding
- Creating transactional data
- An overview of association analysis
- Association Analysis
- Summary
- Test data evaluation
- Regression with MARS
- Creating scores from the components
- Orthogonal rotation and interpretation
- Component extraction
- PCA modeling
- Training and testing datasets
- Data loading and review
- Data
- Rotation
- An overview of the principal components
- Principal Component Analysis
- Summary
- Random forest and PAM
- Gower and PAM
- K-means clustering
- Hierarchical clustering
- Modeling
- Data understanding and preparation
- Dataset background
- Random forest
- PAM
- Gower
- Gower and PAM
- K-means clustering
- Distance calculations
- Hierarchical clustering
- Cluster Analysis
- Summary
- Creating an ensemble
- Random forest model
- Modeling and evaluation
- Data understanding
- Ensembles
- Creating Ensembles and Multiclass Methods
- Summary
- Model training
- Creating the model function
- Loading the data
- Keras and TensorFlow background
- An example of deep learning
- Modeling and evaluation
- Data understanding and preparation
- Creating a simple neural network
- 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
- Extreme gradient boosting – classification
- Random forest
- Classification tree
- Datasets and modeling
- Gradient boosting
- Random forest
- Classification trees
- Understanding a regression tree
- An overview of the techniques
- Tree-Based Classification
- Summary
- Support vector machine
- KNN modeling
- Modeling and evaluation
- Data preparation
- Dataset creation
- Manipulating data
- Support vector machines
- K-nearest neighbors
- K-Nearest Neighbors and Support Vector Machines
- Summary
- Elastic net
- LASSO
- Ridge regression
- Modeling and evaluation
- Data creation
- Elastic net
- LASSO
- Ridge regression
- Regularization overview
- Advanced Feature Selection in Linear Models
- Summary
- Model comparison
- Multivariate adaptive regression splines
- Cross-validation and logistic regression
- Feature selection
- Weight of evidence and information value
- Training a logistic regression algorithm
- Model training and evaluation
- Logistic regression
- Classification methods and linear regression
- Logistic Regression
- Summary
- Reverse transformation of natural log predictions
- Modeling and evaluation – MARS
- Modeling and evaluation – stepwise regression
- Loading and preparing the data
- Multivariate linear regression
- Reviewing model assumptions
- Building a univariate model
- Univariate linear regression
- Linear Regression
- Summary
- Correlation and linearity
- Treating the data
- Zero and near-zero variance features
- Handling missing values
- Exploring categorical variables
- Descriptive statistics
- Handling duplicate observations
- Reading the data
- Overview
- Preparing and Understanding Data
- Reviews
- Get in touch
- Conventions used
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the authors
- Contributors
- Packt.com
- Why subscribe?
- About Packt
- Advanced Machine Learning with R
- Copyright and Credits
- Title Page
- coverpage
- coverpage
- Title Page
- Copyright and Credits
- Advanced Machine Learning with R
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Conventions used
- Get in touch
- Reviews
- Preparing and Understanding Data
- Overview
- Reading the data
- Handling duplicate observations
- Descriptive statistics
- Exploring categorical variables
- Handling missing values
- Zero and near-zero variance features
- Treating the data
- Correlation and linearity
- Summary
- Linear Regression
- Univariate linear regression
- Building a univariate model
- Reviewing model assumptions
- Multivariate linear regression
- Loading and preparing the data
- Modeling and evaluation – stepwise regression
- Modeling and evaluation – MARS
- Reverse transformation of natural log predictions
- Summary
- Logistic Regression
- Classification methods and linear regression
- Logistic regression
- Model training and evaluation
- Training a logistic regression algorithm
- Weight of evidence and information value
- Feature selection
- Cross-validation and logistic regression
- Multivariate adaptive regression splines
- Model comparison
- Summary
- Advanced Feature Selection in Linear Models
- Regularization overview
- Ridge regression
- LASSO
- Elastic net
- Data creation
- Modeling and evaluation
- Ridge regression
- LASSO
- Elastic net
- Summary
- K-Nearest Neighbors and Support Vector Machines
- K-nearest neighbors
- Support vector machines
- Manipulating data
- Dataset creation
- Data preparation
- Modeling and evaluation
- KNN modeling
- Support vector machine
- Summary
- Tree-Based Classification
- An overview of the techniques
- Understanding a regression tree
- Classification trees
- Random forest
- Gradient boosting
- Datasets and modeling
- Classification tree
- Random forest
- Extreme gradient boosting – classification
- 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
- Creating a simple neural network
- Data understanding and preparation
- Modeling and evaluation
- An example of deep learning
- Keras and TensorFlow background
- Loading the data
- Creating the model function
- Model training
- Summary
- Creating Ensembles and Multiclass Methods
- Ensembles
- Data understanding
- Modeling and evaluation
- Random forest model
- Creating an ensemble
- Summary
- Cluster Analysis
- Hierarchical clustering
- Distance calculations
- K-means clustering
- Gower and PAM
- Gower
- PAM
- Random forest
- Dataset background
- Data understanding and preparation
- Modeling
- Hierarchical clustering
- K-means clustering
- Gower and PAM
- Random forest and PAM
- Summary
- Principal Component Analysis
- An overview of the principal components
- Rotation
- Data
- Data loading and review
- Training and testing datasets
- PCA modeling
- Component extraction
- Orthogonal rotation and interpretation
- Creating scores from the components
- Regression with MARS
- Test data evaluation
- Summary
- Association Analysis
- An overview of association analysis
- Creating transactional data
- Data understanding
- Data preparation
- Modeling and evaluation
- Summary
- Time Series and Causality
- Univariate time series analysis
- Understanding Granger causality
- Time series data
- Data exploration
- 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 analysis
- Data overview
- Data frame creation
- Word frequency
- Word frequency in all addresses
- Lincoln's word frequency
- Sentiment analysis
- N-grams
- Topic models
- Classifying text
- Data preparation
- LASSO model
- Additional quantitative analysis
- Summary
- Exploring the Machine Learning Landscape
- ML versus software engineering
- Types of ML methods
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
- Transfer learning
- ML terminology – a quick review
- Deep learning
- Big data
- Natural language processing
- Computer vision
- Cost function
- Model accuracy
- Confusion matrix
- Predictor variables
- Response variable
- Dimensionality reduction
- Class imbalance problem
- Model bias and variance
- Underfitting and overfitting
- Data preprocessing
- Holdout sample
- Hyperparameter tuning
- Performance metrics
- Feature engineering
- Model interpretability
- ML project pipeline
- Business understanding
- Understanding and sourcing the data
- Preparing the data
- Model building and evaluation
- Model deployment
- Learning paradigm
- Datasets
- Summary
- Predicting Employee Attrition Using Ensemble Models
- Philosophy behind ensembling
- Getting started
- Understanding the attrition problem and the dataset
- K-nearest neighbors model for benchmarking the performance
- Bagging
- Bagged classification and regression trees (treeBag) implementation
- Support vector machine bagging (SVMBag) implementation
- Naive Bayes (nbBag) bagging implementation
- Randomization with random forests
- Implementing an attrition prediction model with random forests
- Boosting
- The GBM implementation
- Building attrition prediction model with XGBoost
- Stacking
- Building attrition prediction model with stacking
- Summary
- Implementing a Jokes Recommendation Engine
- Fundamental aspects of recommendation engines
- Recommendation engine categories
- Content-based filtering
- Collaborative filtering
- Hybrid filtering
- Getting started
- Understanding the Jokes recommendation problem and the dataset
- Converting the DataFrame
- Dividing the DataFrame
- Building a recommendation system with an item-based collaborative filtering technique
- Building a recommendation system with a user-based collaborative filtering technique
- Building a recommendation system based on an association-rule mining technique
- The Apriori algorithm
- Content-based recommendation engine
- Differentiating between ITCF and content-based recommendations
- Building a hybrid recommendation system for Jokes recommendations
- Summary
- References
- Sentiment Analysis of Amazon Reviews with NLP
- The sentiment analysis problem
- Getting started
- Understanding the Amazon reviews dataset
- Building a text sentiment classifier with the BoW approach
- Pros and cons of the BoW approach
- Understanding word embedding
- Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
- Building a text sentiment classifier with GloVe word embedding
- Building a text sentiment classifier with fastText
- Summary
- Customer Segmentation Using Wholesale Data
- Understanding customer segmentation
- Understanding the wholesale customer dataset and the segmentation problem
- Categories of clustering algorithms
- Identifying the customer segments in wholesale customer data using k-means clustering
- Working mechanics of the k-means algorithm
- Identifying the customer segments in the wholesale customer data using DIANA
- Identifying the customer segments in the wholesale customers data using AGNES
- Summary
- Image Recognition Using Deep Neural Networks
- Technical requirements
- Understanding computer vision
- Achieving computer vision with deep learning
- Convolutional Neural Networks
- Layers of CNNs
- Introduction to the MXNet framework
- Understanding the MNIST dataset
- Implementing a deep learning network for handwritten digit recognition
- Implementing dropout to avoid overfitting
- Implementing the LeNet architecture with the MXNet library
- Implementing computer vision with pretrained models
- Summary
- Credit Card Fraud Detection Using Autoencoders
- Machine learning in credit card fraud detection
- Autoencoders explained
- Types of AEs based on hidden layers
- Types of AEs based on restrictions
- Applications of AEs
- The credit card fraud dataset
- Building AEs with the H2O library in R
- Autoencoder code implementation for credit card fraud detection
- Summary
- Automatic Prose Generation with Recurrent Neural Networks
- Understanding language models
- Exploring recurrent neural networks
- Comparison of feedforward neural networks and RNNs
- Backpropagation through time
- Problems and solutions to gradients in RNN
- Exploding gradients
- Vanishing gradients
- Building an automated prose generator with an RNN
- Implementing the project
- Summary
- Winning the Casino Slot Machines with Reinforcement Learning
- Understanding RL
- Comparison of RL with other ML algorithms
- Terminology of RL
- The multi-arm bandit problem
- Strategies for solving MABP
- The epsilon-greedy algorithm
- Boltzmann or softmax exploration
- Decayed epsilon greedy
- The upper confidence bound algorithm
- Thompson sampling
- Multi-arm bandit – real-world use cases
- Solving the MABP with UCB and Thompson sampling algorithms
- Summary
- Creating a Package
- Creating a new package
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-06-24 14:25:35
