This hands-on course guides learners through the complete lifecycle of building a movie recommendation system using Python. Beginning with a conceptual overview of recommendation engines and collaborative filtering techniques, learners will identify real-world applications and articulate how these systems drive personalization across platforms. The course progresses through environment setup using Anaconda and dataset preparation, ensuring participants can organize, configure, and manipulate data efficiently.
Recommendation Engine - Basics
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Intermediate level
Recommended experience
1 hour to complete
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Recently updated!
July 2025
Assessments
4 assignments
Taught in English
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There is 1 module in this course
This module introduces learners to the foundational concepts and technical workflow of building a recommendation engine using Python. It begins with a conceptual overview of recommendation systems and collaborative filtering, then transitions into preparing the development environment and datasets using Anaconda and the Surprise library. Finally, learners will construct, evaluate, and deploy a predictive model capable of generating personalized movie recommendations using real user data. The focus is on practical application, model evaluation with cross-validation, and generating top predictions through structured Python functions.
What's included
8 videos4 assignments
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