Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Añadir al carritopaperback. Condición: Very Good. The Art of Feature Engineering: Essentials for Machine Learning This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
Idioma: Inglés
Publicado por Cambridge University Press -, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Añadir al carritoCondición: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press CUP, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Idioma: Inglés
Publicado por Cambridge University Press 6/25/2020, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Añadir al carritoPaperback or Softback. Condición: New. The Art of Feature Engineering: Essentials for Machine Learning. Book.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Añadir al carritoPaperback. Condición: Brand New. 274 pages. 8.75x6.00x0.75 inches. In Stock.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 63,64
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Añadir al carritoPaperback. Condición: 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.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 274 pages. 8.75x6.00x0.75 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: Majestic Books, Hounslow, Reino Unido
EUR 82,07
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Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 82,81
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Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: CitiRetail, Stevenage, Reino Unido
EUR 64,81
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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.
Librería: moluna, Greven, Alemania
EUR 64,81
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Añadir al carritoCondición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 97,44
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.