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prerequisites
basics of Python • basics of pandas • basics of scikit-learn • basics of machine learning • basics of Fast.ai
skills learned
selecting, cleaning and choosing data for collaborative filtering • matrix collaborative filtering techniques using Fast.ai collab_learner class • learning latent factors for collaborative filtering recommenders
Ariel Gamino
1 week · 8-10 hours per week · BEGINNER
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In this liveProject, you’ll create a recommendation engine for an online store using the Fast.ai library. You’ll utilize a dot product and a neural network to come up with the latent factors in a rating matrix, and compare and contrast them to determine which is likely to deliver the best recommendations. You’ll need to select and clean your data, pick the right methods, then create the functions that you need to recommend products based on predicted ratings.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for beginner Python data scientists interested in creating recommendation engines. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Basics of Python
  • Basics of Pandas and dataframe filtering and manipulation
  • Basics of scikit-learn
  • Basics of Fast.ai
TECHNIQUES
  • Basics of machine learning

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