Master the essentials of building recommendation systems from scratch! This course covers collaborative filtering, content-based methods, hybrid techniques, and evaluation metrics through hands-on projects and real-world applications
This course introduces foundational algorithms and concepts that form the backbone of recommendation systems. You'll start with simple baseline prediction models and gradually advance to similarity measures and more sophisticated prediction models. Mastering these fundamentals is essential for developing robust and efficient recommendation tools.
Diving Deep into Collaborative Filtering Techniques with ALS
5 lessons
Course 4
Recommendation Systems Quality Evaluation
4 lessons
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In this course, learners will dive into content-based recommendation systems, focusing on factorization machines and Deep Structured Semantic Models (DSSM). These approaches utilize item features and user profiles to make recommendations. The course provides hands-on coding examples to demonstrate how to develop content-based models that harness rich data for personalized recommendations.
This course explores collaborative filtering techniques, which are central to modern recommendation systems. It covers both user-based and item-based collaborative filtering methods, as well as matrix factorization and the powerful Alternating Least Squares algorithm.
This course focuses on metrics specific to recommendation systems, crucial for evaluating and optimizing model performance. You'll delve into recommendation-specific metrics such as Coverage, Serendipity, Novelty, and Diversity. Each metric is presented with theoretical insights and practical coding examples to illustrate their application.
