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Recommendation Systems Tutorial for Beginners
Created by Stanford and IIT alumni, this Recommender system tutorial teaches collaborative filtering, content-based filtering and movie recommendations in Python enabling you to create your own, personalized, and smart recommendation engines.
Have you ever been impressed by the video recommendations on YouTube or Netflix? This is a result of the work done by their Recommendation Systems. Moreover, the success of an e-commerce website is highly dependent on the efficiency of the Recommendation Engines that they use. The main function of a recommendation engine is to help the user find products that are most relevant to her. Recommendation engines use approaches like content-based filtering, matrix factorization, collaborative filtering, and association rules to make suitable recommendations. This recommendation system tutorial teaches you to do just that.
What will you gain from this course?
- An understanding of the theoretical aspects of recommendation systems in Machine Learning
- An understanding of neighbourhood models and how to confront the challenges that you will encounter while using them
- The ability to identify use-cases for recommendation systems
- The skills required to design and implement recommendation systems in Python
Prerequisites and Target Audience
Having knowledge of undergraduate-level Mathematics will make it easier for you to understand this course, however, it is not a requirement. If you want to run the source code that is given, you will require working knowledge of Python.