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Synopsis
This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single "Cheat Sheet".
The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.
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À propos de l?auteur
Poornachandra Sarang, in his IT career spanning four decades, has been consulting large IT organizations on the design and architecture of systems using state-of-the-art technologies. He has authored several books covering a wide range of emerging technologies. Dr. Sarang is a Ph.D. advisor for Computer Science and Engineering and is on the thesis advisory committee for aspiring doctoral candidates. He has designed and delivered courses/curricula for universities at the postgraduate level, including courses and workshops on emerging technologies for industry. He is a known face at technical and research conferences delivering both keynote and technical talks.
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Thinking Data Science
Edité par
Springer International Publishing Mrz 2024, 2024
ISBN 10 : 303102365X
ISBN 13 : 9783031023651
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development Should I use GOFAI, ANN/DNN or Transfer Learning Can I rely on AutoML for model development What if the client provides me Gig and Terabytes of data for developing analytic models How do I handle high-frequency dynamic datasets This book provides the practitioner with a consolidation of the entire data science process in a single 'Cheat Sheet'.The challenge for a data scientistis to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designedto do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book.Thinking Data Sciencewill helppractising data scientists, academicians, researchers, and students who want to build ML models using theappropriate algorithms and architectures, whether the data be small or big. 380 pp. Englisch. N° de réf. du vendeur 9783031023651
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Thinking Data Science
Edité par
Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10 : 303102365X
ISBN 13 : 9783031023651
Neuf
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on . N° de réf. du vendeur 1384734579
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Thinking Data Science : A Data Science Practitioner's Guide
Edité par
Springer International Publishing, Springer Nature Switzerland, 2024
ISBN 10 : 303102365X
ISBN 13 : 9783031023651
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development Should I use GOFAI, ANN/DNN or Transfer Learning Can I rely on AutoML for model development What if the client provides me Gig and Terabytes of data for developing analytic models How do I handle high-frequency dynamic datasets This book provides the practitioner with a consolidation of the entire data science process in a single 'Cheat Sheet'.The challenge for a data scientistis to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designedto do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book.Thinking Data Sciencewill helppractising data scientists, academicians, researchers, and students who want to build ML models using theappropriate algorithms and architectures, whether the data be small or big. N° de réf. du vendeur 9783031023651
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Thinking Data Science
ISBN 10 : 303102365X
ISBN 13 : 9783031023651
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development Should I use GOFAI, ANN/DNN or Transfer Learning Can I rely on AutoML for model development What if the client provides me Gig and Terabytes of data for developing analytic models How do I handle high-frequency dynamic datasets This book provides the practitioner with a consolidation of the entire data science process in a single ¿Cheat Sheet¿.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 380 pp. Englisch. N° de réf. du vendeur 9783031023651
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Thinking Data Science: A Data Science Practitioner?s Guide (The Springer Series in Applied Machine Learning)
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Thinking Data Science: A Data Science Practitioner?s Guide (The Springer Series in Applied Machine Learning)
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Thinking Data Science: A Data Science Practitioner?s Guide (The Springer Series in Applied Machine Learning)
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