舉報 
會員
Advanced Machine Learning with R
Risoneofthemostpopularlanguageswhenitcomestoexploringthemathematicalsideofmachinelearningandeasilyperformingcomputationalstatistics.ThisLearningPathshowsyouhowtoleveragetheRecosystemtobuildefficientmachinelearningapplicationsthatcarryoutintelligenttaskswithinyourorganization.You'lltacklerealisticprojectssuchasbuildingpowerfulmachinelearningmodelswithensemblestopredictemployeeattrition.You'llexploredifferentclusteringtechniquestosegmentcustomersusingwholesaledataanduseTensorFlowandKeras-Rforperformingadvancedcomputations.You’llalsobeintroducedtoreinforcementlearningalongwithitsvarioususecasesandmodels.Additionally,itshowsyouhowsomeoftheseblack-boxmodelscanbediagnosedandunderstood.BytheendofthisLearningPath,you’llbeequippedwiththeskillsyouneedtodeploymachinelearningtechniquesinyourownprojects.ThisLearningPathincludescontentfromthefollowingPacktproducts:RMachineLearningProjectsbyDr.SunilKumarChinnamgari.MasteringMachineLearningwithR-ThirdEditionbyCoryLesmeister.
目錄(313章)
倒序
- 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 更新時間：2021-06-24 14:25:35
推薦閱讀
- 筆記本電腦使用、維護與故障排除實戰
- 零點起飛學Xilinx FPG
- 基于ARM的嵌入式系統和物聯網開發
- 嵌入式系統設計教程
- 計算機組裝與維修技術
- 基于Apache Kylin構建大數據分析平臺
- 電腦高級維修及故障排除實戰
- 基于PROTEUS的電路設計、仿真與制板
- 新編電腦組裝與硬件維修從入門到精通
- Hands-On Deep Learning for Images with TensorFlow
- Spring Cloud實戰
- 觸摸屏應用技術從入門到精通
- 單片機項目設計教程
- Zabbix 4 Network Monitoring
- 微服務架構基礎（Spring Boot+Spring Cloud+Docker）
- 電腦主板維修技術
- Service Mesh微服務架構設計
- PIC系列單片機的流碼編程
- Learning Microsoft Cognitive Services
- Arduino+3D打印創新電子制作2
- The Complete Guide to DAZ Studio 4
- 計算機組裝與維護項目化教程（第二版）
- Unreal Engine 4 AI Programming Essentials
- 數字噴墨與應用
- Arduino Android Blueprints
- TensorFlow 1.x Deep Learning Cookbook
- Photographic Rendering with VRay for SketchUp
- Cardboard VR Projects for Android
- Unity 2D Game Development
- 筆記本電腦維修寶典
