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Applied Recommender Systems with Python

- Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Af: Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan Engelsk Paperback

Applied Recommender Systems with Python

- Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
Af: Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan Engelsk Paperback
Tjek vores konkurrenters priser

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 Learn

  • Understand and implement different recommender systems techniques with Python
  • Employ 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 filtering
  • Leverage machine learning, NLP, and deep learning for building recommender systems


Who This Book Is For
Data scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
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Tjek vores konkurrenters priser
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kr 431
Fragt: 39 kr
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God 4 anmeldelser på
Tjek vores konkurrenters priser

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 Learn

  • Understand and implement different recommender systems techniques with Python
  • Employ 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 filtering
  • Leverage machine learning, NLP, and deep learning for building recommender systems


Who This Book Is For
Data scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 248
ISBN-13: 9781484289532
Indbinding: Paperback
Udgave:
ISBN-10: 1484289536
Kategori: Machine learning
Udg. Dato: 22 nov 2022
Længde: 0mm
Bredde: 178mm
Højde: 254mm
Forlag: APress
Oplagsdato: 22 nov 2022
Forfatter(e): Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan
Forfatter(e) Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan

Kategori Machine learning

ISBN-13 9781484289532

Sprog Engelsk

Indbinding Paperback

Sider 248

Udgave

Længde 0mm

Bredde 178mm

Højde 254mm

Udg. Dato 22 nov 2022

Oplagsdato 22 nov 2022

Forlag APress

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