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Ensemble Methods Machine Learning by Kunapuli Gautam (33 results)
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Paperback. Condition: As New. It's a preowned item in almost perfect condition. It has no visible cosmetic imperfections. May come without any shrink wrap; pages are clean and not marred by notes or folds of any kind.
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Paperback. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Ensemble Methods for Machine Learning
Seller: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.
Seller rating 4 out of 5 stars
Condition: very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages.
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Ensemble Methods for Machine Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Ensemble Methods for Machine Learning (Paperback)
Published by Manning Publications, New York, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Paperback. Condition: new. Paperback. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Ensemble Methods for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Ensemble Methods for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Ensemble Methods for Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Condition: New.
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Ensemble Methods for Machine Learning
Published by Manning Publications 2023-06-09, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Paperback. Condition: New.
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Condition: New.
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Ensemble Methods for Machine Learning
Published by Manning Publications, US, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Paperback. Condition: New. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.
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Ensemble Methods for Machine Learning
Published by Manning Publications, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
First Edition
Condition: New. 2023. 1st Edition. Paperback. . . . . .
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Ensemble Methods for Machine Learning
Published by Manning Publications, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
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Ensemble Methods for Machine Learning
Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. 350 pp. Englisch.
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Ensemble Methods for Machine Learning
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. 350 pp. Englisch.
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Condition: New.
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Taschenbuch. Condition: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.
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Ensemble Methods for Machine Learning
Published by Manning Publications, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Condition: New. 2023. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.
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Paperback. Condition: Brand New. 350 pages. 9.25x7.37x0.88 inches. In Stock.
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Condition: NEW.
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Ensemble Methods for Machine Learning
Published by Manning Publications, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Kartoniert / Broschiert. Condition: New. Über den AutorrnrnGautam Kunapuli has over 15 years of experience in academia and the machine learning industry. He has developed several novel algorithms for diverse application domains including social network analysis, text and natu.
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Ensemble Methods for Machine Learning
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.Manning, St.-Martin-Straße 82, 81541 München 350 pp. Englisch.
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Taschenbuch. Condition: Neu. Ensemble Methods for Machine Learning | Gautam Kunapuli | Taschenbuch | Kartoniert / Broschiert | Englisch | 2023 | Manning | EAN 9781617297137 | Verantwortliche Person für die EU: Manning, St.-Martin-Str. 82, 81541 München, salesde[at]pearson[dot]com | Anbieter: preigu.
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Ensemble Methods for Machine Learning
Published by Manning Publications, US, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Language: English
Paperback. Condition: New. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.
