Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: Better World Books: West, Reno, NV, Estados Unidos de America
EUR 15,53
Cantidad disponible: 1 disponibles
Añadir al carritoCondición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
EUR 15,53
Cantidad disponible: 1 disponibles
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
Librería: AwesomeBooks, Wallingford, Reino Unido
EUR 33,43
Cantidad disponible: 1 disponibles
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
Librería: Bahamut Media, Reading, Reino Unido
EUR 33,43
Cantidad disponible: 1 disponibles
Añadir al carritopaperback. Condición: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 45,04
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 53,83
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: New.
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 56,10
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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 53,40
Cantidad disponible: 5 disponibles
Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Publicado por Cambridge University Press, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 59,13
Cantidad disponible: 11 disponibles
Añadir al carritoCondición: New.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 53,00
Cantidad disponible: 1 disponibles
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
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 53,40
Cantidad disponible: Más de 20 disponibles
Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 420.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 77,41
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 274 pages. 8.75x6.00x0.75 inches. In Stock.
EUR 57,38
Cantidad disponible: 5 disponibles
Añadir al carritoCondición: New. 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 Jun 2020, 2020
ISBN 10: 1108709389 ISBN 13: 9781108709385
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 69,48
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Neuware - A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.