Hello! Based on the overwhelming popularity of our previous workshop, we’re hosting a repeat session of the Recommendation Systems tutorial for attendees who were unable to make it out to the previous one.
Want to know how Spotify, Amazon, and Netflix create personalized recommendations for their users? In this workshop, we’ll explore various types of recommendation systems, and learn how they’re implemented in Python. For this tutorial, we’ll be using the MovieLens dataset to build our own recommendation system from scratch, which you can download at the following link (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 Collaboratory instead - a free Jupyter notebook environment that runs in the cloud (http://colab.research.google.com/).
Capacity: Due to space constraints, we’re limited to 25 attendees for this meetup.
Special thanks to Demonware 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 centered. We expect everyone attending our meetups to abide by our Code of Conduct (http://www.meetup.com/PyLadies-Vancouver/about/).
This event was originally hosted on Meetup.
View this event on Meetup