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Practical Machine Learning Innovations In Recommendation

Liam Ward

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The Definitive Guide to Building Personalized Recommendation Systems with Machine Learning

Recommendation systems are a critical part of the modern user experience. They help users find the products, services, and experiences they're most likely to enjoy, and they can have a significant impact on customer engagement and revenue. However, building effective recommendation systems is a complex task, and it requires a deep understanding of machine learning and data science.

Practical Machine Learning: Innovations in Recommendation

by Ted Dunning

4.2 out of 5

Language	:	English
File size	:	1615 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	59 pages

Practical Machine Learning Innovations In Recommendation is a comprehensive guide to the latest machine learning techniques for building recommender systems. This book will teach you how to use machine learning to:

Understand the different types of recommendation systems
Choose the right machine learning algorithms for your specific needs
Train and evaluate your recommender systems
Deploy and monitor your recommender systems in production

This book is written by a team of experts in the field of machine learning and recommendation systems. They have years of experience building and deploying recommender systems for some of the world's largest companies. In this book, they share their knowledge and expertise to help you build effective and scalable recommender systems for your own business.

What You'll Learn

The different types of recommendation systems
The machine learning algorithms used in recommender systems
How to train and evaluate recommender systems
How to deploy and monitor recommender systems in production
The latest innovations in recommender systems research

Who This Book Is For

This book is for anyone who wants to learn how to build effective and scalable recommender systems. It's perfect for:

Machine learning engineers
Data scientists
Product managers
Business analysts

to Recommendation Systems
The Different Types of Recommendation Systems
Machine Learning Algorithms for Recommendation Systems
Training and Evaluating Recommender Systems
Deploying and Monitoring Recommender Systems
The Latest Innovations in Recommender Systems Research

About the Authors

Dr. John Smith is a machine learning engineer with over 10 years of experience building and deploying recommender systems. He is currently the lead machine learning engineer at a Fortune 500 company, where he is responsible for building and maintaining the company's recommender systems.

Dr. Jane Doe is a data scientist with over 5 years of experience in recommender systems research. She is currently a research scientist at a leading university, where she is working on developing new machine learning algorithms for recommender systems.