LEADER 03786nam 22006975 450 001 9910632475203321 005 20251008145137.0 010 $a9781484280058 010 $a1484280059 024 7 $a10.1007/978-1-4842-8005-8 035 $a(MiAaPQ)EBC7147130 035 $a(Au-PeEL)EBL7147130 035 $a(CKB)25483682800041 035 $a(OCoLC)1351999040 035 $a(OCoLC-P)1351999040 035 $a(DE-He213)978-1-4842-8005-8 035 $a(PPN)26635369X 035 $a(CaSebORM)9781484280058 035 $a(Perlego)4514126 035 $a(EXLCZ)9925483682800041 100 $a20221125d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Data Analytics Using Python $eWith Architectural Patterns, Text and Image Classification, and Optimization Techniques /$fby Sayan Mukhopadhyay, Pratip Samanta 205 $a2nd ed. 2023. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2023. 215 $a1 online resource (259 pages) 300 $aIncludes index. 311 08$a9781484280041 311 08$a1484280040 327 $aChapter 1: Overview of Python Language -- Chapter 2: ETL with Python -- Chapter 3: Supervised Learning and Unsupervised Learning with Python -- Chapter 4: Clustering with Python -- Chapter 5: Deep Learning & Neural Networks -- Chapter 6: Time Series Analysis -- Chapter 7: Analytics in Scale. 330 $aUnderstand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics withreinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. You will: Build intelligent systems for enterprise Review time series analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analytics Understand Covers design patterns in Python . 606 $aArtificial intelligence$xData processing 606 $aMachine learning 606 $aPython (Computer program language) 606 $aArtificial intelligence 606 $aData Science 606 $aMachine Learning 606 $aPython 606 $aArtificial Intelligence 615 0$aArtificial intelligence$xData processing. 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 0$aArtificial intelligence. 615 14$aData Science. 615 24$aMachine Learning. 615 24$aPython. 615 24$aArtificial Intelligence. 676 $a006.312 700 $aMukhopadhyay$b Sayan$01062767 702 $aSamanta$b Pratip 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910632475203321 996 $aAdvanced Data Analytics Using Python$92982346 997 $aUNINA