Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: California Books, Miami, FL, U.S.A.
EUR 64,33
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Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 61,74
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Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 61,68
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Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 67,49
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Aggiungi al carrelloCondizione: New. 2020. Paperback. . . . . .
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 67,97
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press CUP, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 84,71
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Aggiungi al carrelloCondizione: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.
Da: Revaluation Books, Exeter, Regno Unito
EUR 83,80
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 274 pages. 8.75x6.00x0.75 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: UK BOOKS STORE, London, LONDO, Regno Unito
EUR 103,07
Quantità: 4 disponibili
Aggiungi al carrelloPaperback. Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 84,85
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter not Elektronisches Buch.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: new. Paperback. When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks. This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Revaluation Books, Exeter, Regno Unito
EUR 55,75
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 274 pages. 8.75x6.00x0.75 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Majestic Books, Hounslow, Regno Unito
EUR 82,71
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 82,81
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Da: CitiRetail, Stevenage, Regno Unito
EUR 67,29
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks. This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Da: moluna, Greven, Germania
EUR 64,81
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain a.