Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/473
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dc.contributor.authorAyyadevara, V Kishore. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:19Z-
dc.date.available2020-05-17T08:17:19Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235645 ;en_US
dc.identifier.isbn9781484235638 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/473-
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484235638. ;en_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractBridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning. ;en_US
dc.description.statementofresponsibilityby V Kishore Ayyadevara.en_US
dc.description.tableofcontentsChapter 1: Basics of Machine Learning -- Chapter 2: Linear regression -- Chapter 3: Logistic regression -- Chapter 4: Decision tree -- Chapter 5: Random forest -- Chapter 6: GBM -- Chapter 7: Neural network -- Chapter 8: word2vec -- Chapter 9: Convolutional neural network -- Chapter 10: Recurrent Neural Network -- Chapter 11: Clustering -- Chapter 12: PCA -- Chapter 13: Recommender systems -- Chapter 14: Implementing algorithms in the cloud. ;en_US
dc.format.extentXXI, 362 p. 467 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235638.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectPython. ;en_US
dc.subjectBig Data. ;en_US
dc.subjectOpen Source. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titlePro Machine Learning Algorithmsen_US
dc.title.alternativeA Hands-On Approach to Implementing Algorithms in Python and R /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
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9781484235638.pdf22.74 MBAdobe PDFThumbnail
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Full metadata record
DC FieldValueLanguage
dc.contributor.authorAyyadevara, V Kishore. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:19Z-
dc.date.available2020-05-17T08:17:19Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235645 ;en_US
dc.identifier.isbn9781484235638 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/473-
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484235638. ;en_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractBridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning. ;en_US
dc.description.statementofresponsibilityby V Kishore Ayyadevara.en_US
dc.description.tableofcontentsChapter 1: Basics of Machine Learning -- Chapter 2: Linear regression -- Chapter 3: Logistic regression -- Chapter 4: Decision tree -- Chapter 5: Random forest -- Chapter 6: GBM -- Chapter 7: Neural network -- Chapter 8: word2vec -- Chapter 9: Convolutional neural network -- Chapter 10: Recurrent Neural Network -- Chapter 11: Clustering -- Chapter 12: PCA -- Chapter 13: Recommender systems -- Chapter 14: Implementing algorithms in the cloud. ;en_US
dc.format.extentXXI, 362 p. 467 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235638.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectPython. ;en_US
dc.subjectBig Data. ;en_US
dc.subjectOpen Source. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titlePro Machine Learning Algorithmsen_US
dc.title.alternativeA Hands-On Approach to Implementing Algorithms in Python and R /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484235638.pdf22.74 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAyyadevara, V Kishore. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:17:19Z-
dc.date.available2020-05-17T08:17:19Z-
dc.date.issued2018en_US
dc.identifier.isbn9781484235645 ;en_US
dc.identifier.isbn9781484235638 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/473-
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9781484235638. ;en_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractBridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning. ;en_US
dc.description.statementofresponsibilityby V Kishore Ayyadevara.en_US
dc.description.tableofcontentsChapter 1: Basics of Machine Learning -- Chapter 2: Linear regression -- Chapter 3: Logistic regression -- Chapter 4: Decision tree -- Chapter 5: Random forest -- Chapter 6: GBM -- Chapter 7: Neural network -- Chapter 8: word2vec -- Chapter 9: Convolutional neural network -- Chapter 10: Recurrent Neural Network -- Chapter 11: Clustering -- Chapter 12: PCA -- Chapter 13: Recommender systems -- Chapter 14: Implementing algorithms in the cloud. ;en_US
dc.format.extentXXI, 362 p. 467 illus. ; online resource. ;en_US
dc.publisherApress :en_US
dc.publisherImprint: Apress,en_US
dc.relation.haspart9781484235638.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputersen_US
dc.subjectComputer Scienceen_US
dc.subjectComputing Methodologies. ;en_US
dc.subjectPython. ;en_US
dc.subjectBig Data. ;en_US
dc.subjectOpen Source. ;en_US
dc.subject.ddc006 ; 23 ;en_US
dc.titlePro Machine Learning Algorithmsen_US
dc.title.alternativeA Hands-On Approach to Implementing Algorithms in Python and R /en_US
dc.typeBooken_US
dc.publisher.placeBerkeley, CA :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484235638.pdf22.74 MBAdobe PDFThumbnail
Preview File