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R Machine Learning Projects
Risoneofthemostpopularlanguageswhenitcomestoperformingcomputationalstatistics(statisticalcomputing)easilyandexploringthemathematicalsideofmachinelearning.Withthisbook,youwillleveragetheRecosystemtobuildefficientmachinelearningapplicationsthatcarryoutintelligenttaskswithinyourorganization.Thisbookwillhelpyoutestyourknowledgeandskills,guidingyouonhowtobuildeasilythroughtocomplexmachinelearningprojects.Youwillfirstlearnhowtobuildpowerfulmachinelearningmodelswithensemblestopredictemployeeattrition.Next,you’llimplementajokerecommendationengineandlearnhowtoperformsentimentanalysisonAmazonreviews.You’llalsoexploredifferentclusteringtechniquestosegmentcustomersusingwholesaledata.Inadditiontothis,thebookwillgetyouacquaintedwithcreditcardfrauddetectionusingautoencoders,andreinforcementlearningtomakepredictionsandwinonacasinoslotmachine.Bytheendofthebook,youwillbeequippedtoconfidentlyperformcomplextaskstobuildresearchandcommercialprojectsforautomatedoperations.
目錄(165章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- R Machine Learning Projects
- About Packt
- Why subscribe?
- Packt.com
- Dedication
- Contributors
- About the author
- About the reviewers
- 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
- Download the color images
- Conventions used
- Get in touch
- Reviews
- 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
- The Road Ahead
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-02 14:23:35
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