Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languagesKey FeaturesGain expertise in machine learning, deep learning and other techniquesBuild intelligent end-to-end projects for finance, social media, and a variety of domainsImplement multi-class classification, regression, and clusteringBook DescriptionR is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood.By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.This Learning Path includes content from the following Packt products:R Machine Learning Projects by Dr. Sunil Kumar ChinnamgariMastering Machine Learning with R - Third Edition by Cory LesmeisterWhat you will learnDevelop a joke recommendation engine to recommend jokes that match users’ tastesBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networksMake predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationProduce simple and effective data visualizations for improved insightsUse NLP to extract insights for textImplement tree-based classifiers including random forest and boosted treeWho this book is forIf you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
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This Learning Path is a comprehensive guide that gives you the complete knowledge on how to implement the powerful supervised, unsupervised, and reinforcement learning techniques using R 3.5 in your data science projects. With this Learning Path, you’ll gain insights from complex projects and learn advanced methods to build smart applications.
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Table of ContentsPreparing and Understanding DataLinear RegressionLogistic RegressionAdvanced Feature Selection in Linear ModelsK-Nearest Neighbors and Support Vector MachinesTree-Based ClassificationNeural Networks and Deep LearningCreating Ensembles and Multiclass MethodsCluster AnalysisPrincipal Component AnalysisAssociation AnalysisTime Series and CausalityText MiningExploring the Machine Learning LandscapePredicting Employee Attrition Using Ensemble ModelsImplementing a Joke Recommendation EngineSentiment Analysis of Amazon Reviews with NLPCustomer Segmentation Using Wholesale DataImage Recognition Using Deep Neural NetworksCredit Card Fraud Detection Using AutoencodersAutomatic Prose Generation with Recurrent Neural NetworksWinning the Casino Slot Machines with Reinforcement Learning
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Produktdetaljer
ISBN
9781838641771
Publisert
2019-05-20
Utgiver
Vendor
Packt Publishing Limited
Høyde
93 mm
Bredde
75 mm
Aldersnivå
G, 01
Språk
Product language
Engelsk
Format
Product format
Heftet