LEADER 03952nam 22006855 450 001 9910338013903321 005 20220623183135.0 010 $a9781523150403 010 $a1523150408 010 $a9781484242155 010 $a1484242157 024 7 $a10.1007/978-1-4842-4215-5 035 $a(CKB)4100000007204861 035 $a(MiAaPQ)EBC5614865 035 $a(DE-He213)978-1-4842-4215-5 035 $a(CaSebORM)9781484242155 035 $a(PPN)232967679 035 $a(OCoLC)1085513890 035 $a(OCoLC)on1085513890 035 $a(EXLCZ)994100000007204861 100 $a20181212d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Using R $eWith Time Series and Industry-Based Use Cases in R /$fby Karthik Ramasubramanian, Abhishek Singh 205 $a2nd ed. 2019. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019. 215 $a1 online resource (712 pages) 300 $aIncludes index. 311 08$a9781484242148 311 08$a1484242149 327 $aChapter 1: Introduction to Machine Learning -- Chapter 2: Data Exploration and Preparation -- 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: Time Series Modelling -- Chapter 10: Scalable Machine Learning and related technology -- Chapter 11: Introduction to Deep Learning Models using Keras and TensorFlow. 330 $aExamine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. You will: Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R. 606 $aArtificial intelligence 606 $aOpen source software 606 $aComputer programming 606 $aProgramming languages (Electronic computers) 606 $aR (Computer program language) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aOpen Source$3https://scigraph.springernature.com/ontologies/product-market-codes/I29090 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 615 0$aArtificial intelligence. 615 0$aOpen source software. 615 0$aComputer programming. 615 0$aProgramming languages (Electronic computers) 615 0$aR (Computer program language) 615 14$aArtificial Intelligence. 615 24$aOpen Source. 615 24$aProgramming Languages, Compilers, Interpreters. 676 $a006.31 700 $aRamasubramanian$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 $a9910338013903321 996 $aMachine Learning Using R$92004678 997 $aUNINA