Articoli correlati a Thinking Data Science: A Data Science Practitioner’s...
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2024
- ISBN 10 303102365X
- ISBN 13 9783031023651
- RilegaturaCopertina flessibile
- Numero di pagine380
Compra nuovo
Scopri di più su questo articolo
EUR 50,28
Spese di spedizione:
EUR 23,00
Da: Germania a: U.S.A.
I migliori risultati di ricerca su AbeBooks
Immagini fornite dal venditore
Thinking Data Science
Editore:
Springer International Publishing Mrz 2024
(2024)
ISBN 10: 303102365X
ISBN 13: 9783031023651
Nuovo
Taschenbuch
Quantità: 2
Da:
Valutazione libreria
Descrizione libro Taschenbuch. Condizione: 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. Codice articolo 9783031023651
Compra nuovo
EUR 50,28
Convertire valuta
Immagini fornite dal venditore
Thinking Data Science : A Data Science Practitioner¿s Guide
Editore:
Springer International Publishing
(2024)
ISBN 10: 303102365X
ISBN 13: 9783031023651
Nuovo
Taschenbuch
Quantità: 1
Da:
Valutazione libreria
Descrizione libro Taschenbuch. Condizione: 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. Codice articolo 9783031023651
Compra nuovo
EUR 50,28
Convertire valuta
Immagini fornite dal venditore
Thinking Data Science
Editore:
Springer, Berlin|Springer International Publishing|Springer
(2024)
ISBN 10: 303102365X
ISBN 13: 9783031023651
Nuovo
Brossura
Quantità: > 20
Da:
Valutazione libreria
Descrizione libro Condizione: 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 . Codice articolo 1384734579
Compra nuovo
EUR 55,78
Convertire valuta