Interested in Machine Learning ? This free online course on Recommendation Systems from Google experts is the best place to start.
Google’s 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.
Course content
- Background
- Large-Scale Recommendation Systems
- Terminology
- Recommendation systems overview
- Candidate Generation
- Content-based filtering
- Collaborative filtering and matrix factorization
- Deep neural network models
- Retrieval, scoring, re-ranking
Prerequisites
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.
Course details
- Institution: Google
- Level: Intermediate
- Language: English
- Duration: 4 hours, self-paced
- Provider: Google Developers
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