Da: WorldofBooks, Goring-By-Sea, WS, Regno Unito
EUR 6,20
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
Da: BooksRun, Philadelphia, PA, U.S.A.
Prima edizione
Paperback. Condizione: 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.
EUR 23,64
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
EUR 26,31
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 29,43
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
Condizione: New.
Condizione: As New. Unread book in perfect condition.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
EUR 35,00
Quantità: 15 disponibili
Aggiungi al carrelloCondizione: New. 2022. 1st ed. paperback. . . . . .
Da: Revaluation Books, Exeter, Regno Unito
EUR 36,81
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 35,27
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
EUR 32,31
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Condizione: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.
EUR 35,26
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 36,80
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 38,24
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condizione: 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.
Da: Chiron Media, Wallingford, Regno Unito
EUR 39,01
Quantità: 1 disponibili
Aggiungi al carrellopaperback. Condizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 45,57
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: Revaluation Books, Exeter, Regno Unito
EUR 45,13
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.
ISBN 10: 1484294130 ISBN 13: 9781484294130
Da: SMASS Sellers, IRVING, TX, U.S.A.
Condizione: New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability.
EUR 32,41
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 16,80
Quantità: Più di 20 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
EUR 26,64
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 25,52
Quantità: Più di 20 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: CitiRetail, Stevenage, Regno Unito
EUR 20,84
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. Drowning in the digital noise? This isn't another preachy self-help book. It's not a spiritual sermon wrapped in Sanskrit, and definitely not about quitting social media to live in a cave.Lost in the Scroll is your no-fluff reality check for the digital age. It's where ancient Indian wisdom, yes, Sanatan Dharma, quietly shows up in your everyday chaos, whether you realize it or not.From doomscrolling to dopamine burnout, this book connects the dots between timeless truths and your hyper-online life. No robes, no rituals - just real talk on purpose, peace, and presence in a world designed to distract you.You don't have to ditch your phone.But you might want to rethink what you're really searching for when you scroll.Old wisdom. New context.No preaching. Just perspective. 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: CitiRetail, Stevenage, Regno Unito
EUR 29,78
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Drowning in the digital noise? This isn't another preachy self-help book. It's not a spiritual sermon wrapped in Sanskrit, and definitely not about quitting social media to live in a cave.Lost in the Scroll is your no-fluff reality check for the digital age. It's where ancient Indian wisdom, yes, Sanatan Dharma, quietly shows up in your everyday chaos, whether you realize it or not.From doomscrolling to dopamine burnout, this book connects the dots between timeless truths and your hyper-online life. No robes, no rituals - just real talk on purpose, peace, and presence in a world designed to distract you.You don't have to ditch your phone.But you might want to rethink what you're really searching for when you scroll.Old wisdom. New context.No preaching. Just perspective. 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 40,39
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 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.