LEADER 04196nam 22006855 450 001 9910156188103321 005 20220628185845.0 024 7 $a10.1007/978-1-4842-2334-5 035 $a(CKB)3710000000985003 035 $a(DE-He213)978-1-4842-2334-5 035 $a(MiAaPQ)EBC4773695 035 $a(CaSebORM)9781484223345 035 $a(PPN)19745951X 035 $a(OCoLC)1005138970 035 $a(OCoLC)on1005138970 035 $a(EXLCZ)993710000000985003 100 $a20161223d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Using R /$fby Karthik Ramasubramanian, Abhishek Singh 205 $a1st ed. 2017. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2017. 215 $a1 online resource (XXIII, 566 p. 209 illus., 155 illus. in color.) 311 08$a9781484223345 311 08$a1484223349 311 08$a9781484223338 311 08$a1484223330 327 $aChapter 1: Introduction to Machine Learning and R -- Chapter 2: Data Preparation and Exploration -- Chapter 3: Sampling and Resampling Techniques -- Chapter 4: Visualization of Data -- Chapter 5: Feature Engineering -- Chapter 6: Machine Learning Models: Theory and Practice -- Chapter 7: Machine Learning Model Evaluation.-Chapter 8: Model Performance Improvement -- Chapter 9: Scalable Machine Learning and related technology.-. 330 $aThis book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data. This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots. For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R. All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data. 517 3 $aComprehensive guide to machine learning 606 $aArtificial intelligence 606 $aComputer programming 606 $aProgramming languages (Electronic computers) 606 $aDatabase management 606 $aR (Computer language program) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aProgramming languages (Electronic computers) 615 0$aDatabase management. 615 0$aR (Computer language program). 615 14$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aDatabase Management. 676 $a006 700 $aRamasubramanian$b Karthik$4aut$4http://id.loc.gov/vocabulary/relators/aut$0897301 702 $aSingh$b Abhishek$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910156188103321 996 $aMachine Learning Using R$92004678 997 $aUNINA