Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/1994
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dc.contributor.authorMukhopadhyay, Sayan. ; author. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:34:57Z-
dc.date.available2020-05-17T08:34:57Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/1994-
dc.descriptionQA76en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484234495 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractGain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.  After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. You will: Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP. ;en_US
dc.description.statementofresponsibilityby Sayan Mukhopadhyay.en_US
dc.description.tableofcontentsChapter 1: Introduction -- Chapter 2: ETL with Python -- Chapter 3: Supervised Learning with Python -- Chapter 4: Unsupervised Learning with Python -- Chapter 5: Deep Learning & Neural Networks -- Chapter 6: Time Series Analysis -- Chapter 7: Python in Emerging Technologies. ;en_US
dc.format.extentXV, 186 p. 18 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484234501.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Scienceen_US
dc.subjectPython. ;en_US
dc.subjectBig Data. ;en_US
dc.subjectOpen Source. ;en_US
dc.titleAdvanced Data Analytics Using Pythonen_US
dc.title.alternativeWith Machine Learning, Deep Learning and NLP Examples /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مهندسی فناوری اطلاعات

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Full metadata record
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dc.contributor.authorMukhopadhyay, Sayan. ; author. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:34:57Z-
dc.date.available2020-05-17T08:34:57Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/1994-
dc.descriptionQA76en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484234495 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractGain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.  After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. You will: Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP. ;en_US
dc.description.statementofresponsibilityby Sayan Mukhopadhyay.en_US
dc.description.tableofcontentsChapter 1: Introduction -- Chapter 2: ETL with Python -- Chapter 3: Supervised Learning with Python -- Chapter 4: Unsupervised Learning with Python -- Chapter 5: Deep Learning & Neural Networks -- Chapter 6: Time Series Analysis -- Chapter 7: Python in Emerging Technologies. ;en_US
dc.format.extentXV, 186 p. 18 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484234501.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Scienceen_US
dc.subjectPython. ;en_US
dc.subjectBig Data. ;en_US
dc.subjectOpen Source. ;en_US
dc.titleAdvanced Data Analytics Using Pythonen_US
dc.title.alternativeWith Machine Learning, Deep Learning and NLP Examples /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484234501.pdf2.24 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMukhopadhyay, Sayan. ; author. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:34:57Z-
dc.date.available2020-05-17T08:34:57Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/1994-
dc.descriptionQA76en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484234495 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractGain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.  After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. You will: Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP. ;en_US
dc.description.statementofresponsibilityby Sayan Mukhopadhyay.en_US
dc.description.tableofcontentsChapter 1: Introduction -- Chapter 2: ETL with Python -- Chapter 3: Supervised Learning with Python -- Chapter 4: Unsupervised Learning with Python -- Chapter 5: Deep Learning & Neural Networks -- Chapter 6: Time Series Analysis -- Chapter 7: Python in Emerging Technologies. ;en_US
dc.format.extentXV, 186 p. 18 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484234501.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Scienceen_US
dc.subjectPython. ;en_US
dc.subjectBig Data. ;en_US
dc.subjectOpen Source. ;en_US
dc.titleAdvanced Data Analytics Using Pythonen_US
dc.title.alternativeWith Machine Learning, Deep Learning and NLP Examples /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484234501.pdf2.24 MBAdobe PDFThumbnail
Preview File