Order Total (1 Item Items):
Shipping Destination:
Krishnan Adithya (33 results)
Skip to main search results
Search filters
Product Type
- All Product Types
- Books (33)
- Magazines & Periodicals (No further results match this refinement)
- Comics (No further results match this refinement)
- Sheet Music (No further results match this refinement)
- Art, Prints & Posters (No further results match this refinement)
- Photographs (No further results match this refinement)
- Maps (No further results match this refinement)
- Manuscripts & Paper Collectibles (No further results match this refinement)
Condition Learn more
- New (27)
- As New, Fine or Near Fine (4)
- Very Good or Good (2)
- Fair or Poor (No further results match this refinement)
- As Described (No further results match this refinement)
Binding
Collectible Attributes
- First Edition (2)
- Signed (No further results match this refinement)
- Dust Jacket (No further results match this refinement)
- Seller-Supplied Images (13)
- Not Print on Demand (30)
Language (2)
Free Shipping
Seller Location
Seller Rating
-
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Language: English
Published by Apress (edition 1st ed.), 2022
ISBN 10: 1484289773 ISBN 13: 9781484289778
First Edition
Paperback. Condition: Good. 1st ed. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.
-
Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Seller rating 5 out of 5 stars
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.
-
Condition: New.
-
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
-
Lost in the Scroll
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
US$ 19.19
US$ 5.58 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
-
Condition: New.
-
Condition: New.
-
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
-
Condition: As New. Unread book in perfect condition.
-
-
Applied Recommender Systems with Python
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
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.
-
-
Lost in the Scroll
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
US$ 29.30
US$ 5.58 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
-
-
Condition: As New. Unread book in perfect condition.
-
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
First Edition
Condition: New. 2022. 1st ed. paperback. . . . . .
-
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 40.19
US$ 16.06 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
US$ 43.43
US$ 13.41 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.
-
Time Series Algorithms Recipes 1st ed.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 38.10
US$ 20.11 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketCondition: As New. Unread book in perfect condition.
-
Time Series Algorithms Recipes 1st ed.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 40.17
US$ 20.11 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketCondition: New.
-
Condition: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.
-
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 42.66
US$ 20.11 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
-
International EditionTime Series Algorithms Recipes
ISBN 10: 1484294130 ISBN 13: 9781484294130
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.
-
Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 45.31
US$ 20.11 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
-
US$ 45.22
US$ 20.77 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketpaperback. Condition: New.
-
Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 51.93
US$ 16.06 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
-
US$ 53.25
US$ 16.76 shipping
Ships from United Kingdom to U.S.A.Quantity: 2 available
Add to basketPaperback. Condition: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.
-
Time Series Algorithms Recipes
Language: English
Published by Springer, Berlin|Apress, 2023
ISBN 10: 1484289773 ISBN 13: 9781484289778
Condition: New.
-
Buch. Condition: Neu. Neuware - 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.
-
US$ 30.88
US$ 87.16 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. 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.
