Building a recommendation system from scratch

Want to know how Spotify, Amazon, and Netflix create personalized recommendations for their users? In this workshop, we will explore various types of recommendation systems, and learn how they are implemented in Python. We will use the MovieLens dataset to build our own recommendation system from scratch (http://grouplens.org/datasets/movielens/).

Skill level: Intermediate. You should be comfortable with Python and know how to use libraries such as numpy, pandas, and scikit-learn.

Minimal setup: Bring your laptop if you want to participate in the tutorial. We’ll be using Python 3.6, Jupyter Lab, and several Python packages including numpy, pandas, scikit-learn, matplotlib, and seaborn. If you don't have these dependencies installed locally, you can use Google Colaboratory - a free Jupyter notebook environment that runs in the cloud (http://colab.research.google.com/).

Special thanks to Telus Digital for the meetup space and supporting PyLadies Vancouver.

Persons with any gender identity are welcome, and should respect that PyLadies Vancouver must be a place where women's voices are centred. We expect everyone attending our meetups to abide by our Code of Conduct (https://www.meetup.com/PyLadies-Vancouver/about/).