LEADER 04103oam 2200553 450 001 9910585795503321 005 20221111164633.0 010 $a9781119776499$b(ebk) 010 $a111977649X$b(ebk) 010 $a9781119776482 010 $a1119776481 010 $a1-119-77649-X 010 $a1-119-77648-1 035 $a(MiAaPQ)EBC7046903 035 $a(Au-PeEL)EBL7046903 035 $a(CKB)24267614300041 035 $a(EXLCZ)9924267614300041 100 $a20220721d2022 uy 0 101 0 $aeng 135 $aurcz#---auuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning and data science $efundamentals and applications /$fedited by Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan and Nisheeth Joshi 210 1$aHoboken, NJ :$cWiley ;$aBeverly, MA :$cScrivener Publishing,$d2022. 210 4$dİ2022. 215 $a1 online resource (271 pages) 225 0 $aAdvances in data engineering and machine learning 311 08$aPrint version: Agrawal, Prateek Machine Learning and Data Science Newark : John Wiley & Sons, Incorporated,c2022 9781119775614 320 $aIncludes bibliographical references and index. 327 $aCover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Book Description -- 1 Machine Learning: An Introduction to Reinforcement Learning -- 1.1 Introduction -- 1.1.1 Motivation -- 1.1.2 Machine Learning -- 1.1.3 How Machines Learn -- 1.1.4 Analogy -- 1.1.5 Reinforcement Learning Process -- 1.1.6 Reinforcement Learning Definitions: Basic Terminologies -- 1.1.7 Reinforcement Learning Concepts -- 1.2 Reinforcement Learning Paradigm: Characteristics -- 1.3 Reinforcement Learning Problem -- 1.4 Applications of Reinforcement Learning -- Conclusion -- References; 2 Data Analysis Using Machine Learning: An Experimental Study on UFC -- 2.1 Introduction -- 2.2 Proposed Methodology -- 2.2.1 Data Extraction: Preliminary -- 2.2.2 Pre-Processing Dataset -- 2.3 Experimental Evaluation and Visualization -- 2.4 Conclusion -- References -- 3 Dawn of Big Data with Hadoop and Machine Learning -- 3.1 Introduction -- 3.2 Big Data -- 3.2.1 The Life Cycle of Big Data -- 3.2.2 Challenges in Big Data -- 3.2.3 Scaling in Big Data Platforms -- 3.2.4 Factors to Understand Big Data Platforms and Their Selection Criteria -- 3.2.5 Current Trends in Big Data; 3.2.6 Big Data Use Cases -- 3.3 Machine Learning -- 3.3.1 Machine Learning Algorithms -- 3.4 Hadoop -- 3.4.1 Components of the Hadoop Ecosystem -- 3.4.2 Other Important Components of the Hadoop Ecosystem for Machine Learning -- 3.4.3 Benefits of Hadoop with Machine Learning -- 3.5 Studies Representing Applications of Machine Learning Techniques with Hadoop -- 3.6 Conclusion -- References -- 4 Industry 4.0: Smart Manufacturing in Industries The Future -- 4.1 Introduction -- Challenges or Responses -- Shared Infrastructure -- Security -- Costs or Profitability -- Future Proofing -- Conclusion; 6.3.3 Cluster-Based Mapping with Depth First Search (DFS) Algorithm -- 6.4 Proposed Methodology -- 6.4.1 Cluster-Based Mapping with FM Algorithm -- 6.4.2 Calculation of Total Power Consumption -- 6.4.3 Total Power Calculation by Using Tabu Search -- 6.5 Experimental Results and Discussion -- 6.5.1 Total Power Consumption in 2D NoC -- 6.5.2 Performance of Tabu Search for Power Optimization with Mesh Topology -- 6.5.3 Performance of Tabu Search for Power Optimization with Ring Topology -- 6.5.4 Average Hop Counts for 2D NoC -- 6.6 Conclusion -- References 606 $aMachine learning 606 $aData mining 608 $aElectronic books. 615 0$aMachine learning. 615 0$aData mining. 676 $a006.3/1 702 $aAgrawal$b Prateek 702 $aGupta$b Charu 702 $aSharma$b Anand 702 $aMadaan$b Vishu 702 $aJoshi$b Nisheeth 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910585795503321 996 $aMachine learning and data science$92963040 997 $aUNINA