Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/469
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dc.contributor.authorSarkar, Dipanjan. ;en_US
dc.contributor.authorBali, Raghav. ;en_US
dc.contributor.authorSharma, Tushar. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:17Z-
dc.date.available2020-05-17T08:17:17Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484232071 ;en_US
dc.identifier.isbn9781484232064 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/469-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484232064. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractMaster the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. e The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.ePractical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling.ePart 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance.e Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. ;en_US
dc.description.statementofresponsibilityby Dipanjan Sarkar, Raghav Bali, Tushar Sharma.en_US
dc.description.tableofcontentsChapter 1: eMachine Learning Basics -- Chapter 2: eThe Python Machine Learning Ecosystem -- Chapter 3: Processing, Wrangling and Visualizing Data.-Chapter 4: eFeature Engineering and Selection -- Chapter 5: Building, Tuning and Deploying Models.-Chapter 6: Analyzing Bike Sharing Trends -- Chapter 7: Analyzing Movie Reviews Sentiment -- Chapter 8: Customer Segmentation and Effective Cross Selling -- Chapter 9:eAnalyzing Wine Types and Quality -- Chapter 10: Analyzing Music Trends and Recommendations -- Chapter 11: Forecasting Stock and Commodity Prices -- Chapter 12: Deep Learning for Computer Vision. ;en_US
dc.format.extentXXV, 530 p. 273 illus., 209 illus. in color. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484232064.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectPython. ;en_US
dc.subjectOpen Source. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titlePractical Machine Learning with Pythonen_US
dc.title.alternativeA Problem-Solver's Guide to Building Real-World Intelligent Systems /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

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9781484232064.pdf19.86 MBAdobe PDFThumbnail
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Full metadata record
DC FieldValueLanguage
dc.contributor.authorSarkar, Dipanjan. ;en_US
dc.contributor.authorBali, Raghav. ;en_US
dc.contributor.authorSharma, Tushar. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:17Z-
dc.date.available2020-05-17T08:17:17Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484232071 ;en_US
dc.identifier.isbn9781484232064 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/469-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484232064. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractMaster the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. e The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.ePractical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling.ePart 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance.e Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. ;en_US
dc.description.statementofresponsibilityby Dipanjan Sarkar, Raghav Bali, Tushar Sharma.en_US
dc.description.tableofcontentsChapter 1: eMachine Learning Basics -- Chapter 2: eThe Python Machine Learning Ecosystem -- Chapter 3: Processing, Wrangling and Visualizing Data.-Chapter 4: eFeature Engineering and Selection -- Chapter 5: Building, Tuning and Deploying Models.-Chapter 6: Analyzing Bike Sharing Trends -- Chapter 7: Analyzing Movie Reviews Sentiment -- Chapter 8: Customer Segmentation and Effective Cross Selling -- Chapter 9:eAnalyzing Wine Types and Quality -- Chapter 10: Analyzing Music Trends and Recommendations -- Chapter 11: Forecasting Stock and Commodity Prices -- Chapter 12: Deep Learning for Computer Vision. ;en_US
dc.format.extentXXV, 530 p. 273 illus., 209 illus. in color. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484232064.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectPython. ;en_US
dc.subjectOpen Source. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titlePractical Machine Learning with Pythonen_US
dc.title.alternativeA Problem-Solver's Guide to Building Real-World Intelligent Systems /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484232064.pdf19.86 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSarkar, Dipanjan. ;en_US
dc.contributor.authorBali, Raghav. ;en_US
dc.contributor.authorSharma, Tushar. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:17Z-
dc.date.available2020-05-17T08:17:17Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484232071 ;en_US
dc.identifier.isbn9781484232064 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/469-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484232064. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractMaster the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. e The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.ePractical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling.ePart 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance.e Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. ;en_US
dc.description.statementofresponsibilityby Dipanjan Sarkar, Raghav Bali, Tushar Sharma.en_US
dc.description.tableofcontentsChapter 1: eMachine Learning Basics -- Chapter 2: eThe Python Machine Learning Ecosystem -- Chapter 3: Processing, Wrangling and Visualizing Data.-Chapter 4: eFeature Engineering and Selection -- Chapter 5: Building, Tuning and Deploying Models.-Chapter 6: Analyzing Bike Sharing Trends -- Chapter 7: Analyzing Movie Reviews Sentiment -- Chapter 8: Customer Segmentation and Effective Cross Selling -- Chapter 9:eAnalyzing Wine Types and Quality -- Chapter 10: Analyzing Music Trends and Recommendations -- Chapter 11: Forecasting Stock and Commodity Prices -- Chapter 12: Deep Learning for Computer Vision. ;en_US
dc.format.extentXXV, 530 p. 273 illus., 209 illus. in color. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484232064.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectPython. ;en_US
dc.subjectOpen Source. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titlePractical Machine Learning with Pythonen_US
dc.title.alternativeA Problem-Solver's Guide to Building Real-World Intelligent Systems /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484232064.pdf19.86 MBAdobe PDFThumbnail
Preview File