This Advanced Recommender Systems course is the perfect opportunity to learn how to use advanced machine learning techniques to build more sophisticated recommender systems. You will learn how to manage hybrid information, combine different filtering techniques, use factorization machines, identify new trends and challenges, and create new or significantly improved recommendation tools. With this course, you will be able to design more sophisticated recommender systems and solve the cross-domain recommendation problem. Take advantage of this opportunity to develop your creativity and innovation skills and create new or significantly improved recommendation tools.
Building a Personalised Attrition Recommendation System
1.5
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This course will help you build a personalised attrition recommendation system. It will cover topics such as setting up a business case, exploring data, correlation analysis, developing a recommendation strategy and implementing an attrition predictor. You will also learn how to get your point across and answer any questions you may have. This course is perfect for anyone looking to gain a better understanding of attrition and how to develop a personalised recommendation system.
Evaluation Measures for Search and Recommender Systems
2.5
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This course provides an introduction to evaluation measures for search and recommender systems. It covers topics such as offline metrics, dataset and retrieval 101, [email protected], MRR, [email protected], [email protected], and [email protected]. It also provides an overview of the pros and cons of each evaluation measure. The course is taught in Python and provides a comprehensive overview of the evaluation measures used in search and recommender systems. It is perfect for anyone looking to gain a better understanding of these systems and how to evaluate them.
Basic Recommender Systems
1.5
Coursera0 learners
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This course introduces you to the leading approaches in recommender systems. After completing it, you'll be able to design recommender systems tailored for new application domains, also considering surrounding social and ethical issues. You'll learn how to distinguish recommender systems according to their input data, their internal working mechanisms, and their goals. You'll also have the tools to measure the quality of a recommender system and to incrementally improve it with the design of new algorithms. Unleash your creativity and innovation skills to design a new recommender system and improve the quality of the predictions. Click now to learn more!
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