In this final BLU in the Recommender Systems (RS) specialization we learn taxonomy and strategies to extend the algorithms and techniques learned so far. We also learn about evaluation metrics and Surprise, for fast prototyping. Finally, we zoom in on building RS based on implicit feedback and unary data. Good luck!
- Install all the needed dependencies, specified in the requirements.txt file
- Install via pip
pip install -r requirements.txt
- Go through the Learning Notebooks (they are in the Learning Notebooks/ folder)
- Do the Exercise notebook, and submit it on the portal as usual
You can and should ask for help, be it about Learning Notebooks, Exercises, or anything else. Please check out the How to Ask for Help, and remember not to share code when asking for help about the exercises!
This repo is entirely open source and is continuously improving over time. When you spot a mistake, check the issues. If it hasn't, please open an issue, explaining in details where it is (e.g., in what notebook, and on what line), and how to reproduce the error. If it is an easy fix, feel free to make a pull request.