Navigate the World of Data Science with Practical Recommender System Techniques. Enhance Your Skills Today!
Sub Category
- Data Science
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Objectives
- Comprehend the principles and implementation of content-based recommendation engines.
- Evaluate the performance of recommendation models using RMSE and MAE.
- Apply matrix factorization models using RapidMiner for rating prediction.
- Understand the key parameters in matrix factorization for recommendation engines.
- Analyze the significance of latent factors in collaborative filtering.
- Implement content-based filtering to recommend items based on user preferences.
- Utilize decision trees for personalized recommendation models.
- Build and update user profiles for effective content-based recommendations.
Pre Requisites
- Basic understanding of data concepts.
- Familiarity with programming (Python recommended but not mandatory).
- Access to a computer with internet connectivity.
- Interest in data science and recommender systems.
- No prior experience required; suitable for beginners.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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