LEADER 03537nam 2200541 450 001 9910677891003321 005 20220422110803.0 010 $a1-5231-3319-8 010 $a1-119-59157-0 010 $a1-119-59153-8 010 $a1-119-59154-6 035 $a(CKB)4100000010870980 035 $a(MiAaPQ)EBC6174019 035 $a(CaSebORM)9781119591511 035 $a(PPN)270072101 035 $a(EXLCZ)994100000010870980 100 $a20200730h20202020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPractical machine learning in R /$fFred Nwanganga, Mike Chapple 210 1$aIndianapolis :$cJohn Wiley and Sons,$d[2020] 210 4$aŠ2020 215 $a1 online resource (466 pages) $cillustrations 300 $aIncludes index. 311 $a1-119-59151-1 330 $aGuides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning?a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions?allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world 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 XGBoost Practical Machine Learning in R is a must-have 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. 606 $aMachine learning 606 $aR (Computer program language) 606 $aAprenentatge automātic$2thub 606 $aR (Llenguatge de programaciķ)$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 0$aR (Computer program language) 615 7$aAprenentatge automātic 615 7$aR (Llenguatge de programaciķ) 676 $a617.9 700 $aNwanganga$b Fred Chukwuka$01345965 702 $aChapple$b Mike 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910677891003321 996 $aPractical machine learning in R$93071822 997 $aUNINA