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Can I read Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition on EtoBox?
Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition by Brett Lantz is a science book available to read on EtoBox.
What is Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition about?
Key FeaturesBook DescriptionWhat you will learnHarness the power of R to build common machine learning algorithms with realworld data science applicationsGet to grips with techniques in R to clean and prepare your data for analysis and visualize your resultsDiscover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problemsClassify your data with Bayesian and nearest neighbour methodsPredict values using R to build decision trees, rules, and support vector machinesForecast numeric values with linear regression and model your data with neural networksEvaluate and improve the performance of machine learning modelsLearn specialized machine learning techniques for text mining, social network data, and big dataWho this book is forPerhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Who reads Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Brett Lantz
- Publisher
- Packt Publishing Limited
- Published
- 2015
- Language
- EN
- ISBN
- 9781784394523
- Category
- science
- Subjects
- Computer Science, Mathematics, Stem
- Rating
- 4.4 / 5 (127 ratings)
- Updated
- 2026-03-14
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