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Solve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learnDiscover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks ― the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlowWho this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R. Table of ContentsIntroducing Machine LearningManaging and Understanding DataLazy Learning – Classification Using Nearest NeighborsProbabilistic Learning – Classification Using Naive BayesDivide and Conquer – Classification Using Decision Trees and RulesForecasting Numeric Data – Regression MethodsBlack Box Methods – Neural Networks and Support Vector MachinesFinding Patterns – Market Basket Analysis Using Association RulesFinding Groups of Data – Clustering with k-meansEvaluating Model PerformanceImproving Model PerformanceSpecialized Machine Learning Topics

Details e-book Machine Learning with R

🗸 Author(s): Brett Lantz
🗸 Title: Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
🗸 Rating : 4.6 from 5 stars (228 reviews)
🗸 Languange: English
🗸 Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
🗸 Supported Devices: Android, iOS, PC and Amazon Kindle

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