In this comprehensive course, you will delve into the realm of advanced recommender systems, harnessing the power of machine learning to construct more sophisticated recommendation models. Through a series of engaging modules, you will master the integration of diverse filtering techniques, hybrid information management, and the incorporation of side information for context-aware recommendations.
By the end of this course, you will possess the skills to design and implement cutting-edge recommender systems capable of solving complex cross-domain recommendation challenges, leveraging your creativity and innovation skills to drive impactful outcomes.
Certificate Available ✔Get Started / More Info
The course comprises modules on advanced collaborative filtering, singular value decomposition techniques, hybrid and context-aware recommender systems, factorization machines, and the Recsys Challenge (Honors), offering a comprehensive exploration of advanced recommender system development.
This module provides an overview of advanced collaborative filtering, delving into item-based collaborative filtering as an optimization problem and exploring SLIM, Bayesian Probabilistic Ranking, and more. The module concludes with a comprehensive course syllabus, acknowledgments, and a graded assessment.
Explore singular value decomposition (SVD) techniques in this module, covering matrix factorization, Funk SVD, SVD++, Asymmetric SVD, and Pure SVD. Additionally, the module delves into explaining the model and explores recommendation items and explainability in machine learning, followed by a graded assessment.
This module focuses on hybrid and context-aware recommender systems, covering linear and list combinations, pipelining, merging models, collaborative filtering with side information, and context-aware recommender systems. It concludes with a graded assessment covering tensor-based factorization, preferences in context, and a matter of weights.
Delve into factorization machines in this module, exploring the core concepts, extending the model, and solving imbalance problems. The module concludes with a graded assessment covering factorization machines and multimedia contents.
This honors module presents the Recsys Challenge, offering an opportunity to apply the knowledge and skills gained throughout the course to real-world challenges in recommender system development.
Applied Data Science is a 4-course Specialization by IBM, providing hands-on skills in Python, data analysis, and visualization for making data-driven business decisions....
Exploring machine learning models
Machine Learning Models in Science provides a comprehensive overview of applying machine learning techniques to scientific problems, focusing on data preprocessing,...
This guided project course provides hands-on experience in creating and training convolutional neural networks with TensorFlow for real-world image analysis tasks....