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!

This course, Recommender Systems: Behind the Screen, is a great opportunity to explore and learn the best methods and practices in recommender systems. It is developed by IVADO, HEC Montréal and Université de Montréal and guided by seven international experts from both Academia and Industry. The course is designed for industry professionals and academics with basic knowledge in mathematics and programming, and graduate students in science and engineering. It covers topics such as machine learning, evaluation methods, advanced modelling, contextual bandits, ranking methods and fairness and discrimination in recommender systems. It takes 6 weeks to complete the course and there are comprehensive quizzes and tutorials to evaluate your understanding. Register now and join this special learning journey!

This course is perfect for anyone interested in learning about Recommender Systems. It covers the basic concept, Collaborative Filtering, Recommender System with Deep Learning, and Further Issues of Recommender Systems. It requires basic knowledge of Python programming and mathematics including matrix multiplications, conditional probability, and basic machine learning algorithms. With this course, you will gain a comprehensive understanding of the fundamentals of Recommender Systems and be able to apply them to real-world problems. So, if you are looking to learn more about Recommender Systems, this course is for you.

This course will teach you how to build a music recommendation engine using the AudiSet dataset, embedding generator, and ANNOY. You will learn how to generate embedding from WAV files, process AudioSet data, and understand the ANNOY algorithm. Finally, you will be able to code a recommendation engine with ANNOY. This course is perfect for anyone interested in learning how to build a music recommendation engine.