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Practical Machine Learning in R,Used
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Guides professionals and students through the rapidly growing field of machine learning with handson examples in the popular R programming languageMachine learninga branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructionsallows organizations to reveal patterns in their data and incorporate predictive analytics into their decisionmaking process. Practical Machine Learning in R provides a handson approach to solving business problems with intelligent, selflearning computer algorithms.Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide handson examples created in the R programming language. A perfect guide for professional selftaught learners or students in an introductory machine learning course, this readerfriendly book illustrates the numerous realworld business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoostPractical Machine Learning in R is a musthave guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.
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