Welcome to Recommendation Systems! We’ve designed this course to expand your knowledge of recommendation systems and explain different models used in recommendation, including matrix factorization and deep neural networks.
Interested in Machine Learning ? This free online course on Recommendation Systems from Google experts is for you.
Recommendation Systems course description
What you will learn:
- Describe the purpose of recommendation systems.
- Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.
- Use embeddings to represent items and queries.
- Develop a deeper technical understanding of common techniques used in candidate generation.
- Use TensorFlow to develop two models used for recommendation: matrix factorization and softmax.
- Large-Scale Recommendation Systems
- Recommendation systems overview
- Candidate Generation
- Content-based filtering
- Collaborative filtering and matrix factorization
- Deep neural network models
- Retrieval, scoring, re-ranking
This course assumes you have:
- Completed Machine Learning Crash Course either in-person or self-study, or you have equivalent knowledge.
- Familiarity with linear algebra (inner product, matrix-vector product).
- At least a little experience programming with TensorFlow and pandas.