Language: English
Published by Apress (edition 1st ed.), 2022
ISBN 10: 1484289773 ISBN 13: 9781484289778
Seller: BooksRun, Philadelphia, PA, U.S.A.
First Edition
Paperback. Condition: Very Good. 1st ed. 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.
Condition: New.
Condition: As New. Unread book in perfect condition.
Paperback. Condition: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
Condition: New.
Condition: As New. Unread book in perfect condition.
Condition: New.
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
First Edition
Condition: New. 2022. 1st ed. paperback. . . . . .
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
Condition: As New. Unread book in perfect condition.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 29.60
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Condition: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
Seller: Chiron Media, Wallingford, United Kingdom
paperback. Condition: New.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Seller: SMASS Sellers, IRVING, TX, U.S.A.
Condition: New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 38.24
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Paperback. Condition: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Seller: SMASS Sellers, IRVING, TX, U.S.A.
Condition: New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability.
Condition: New. 1st ed. edition NO-PA16APR2015-KAP.
Language: English
Published by Springer, Berlin|Apress, 2023
ISBN 10: 1484289773 ISBN 13: 9781484289778
Seller: moluna, Greven, Germany
Condition: New.
Paperback. Condition: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Language: English
Published by Springer, Berlin|Apress, 2023
ISBN 10: 1484289536 ISBN 13: 9781484289532
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You ll start by learning basic concepts of recommende.