Join Lillian Pierson, P.E. for an in-depth discussion in this video Using the exercise files, part of Building a Recommendation System with Python Machine Learning & AI.
- [Narrator] There are a set of exercise files that come with the course, and so I just want to take a quick minute to show you how to use those. You're going to get a zipped file that looks something like this and you want to unzip the exercise files. When you unzip the exercise file folder and open it up, you're going to see something that looks like this. There's a system of sub folders labeled by the names of the segments to which they correspond for the course.
Inside each of these sub folders, you're going to see an IPython Notebook file that accompanies the demo. To use this file, just launch your Jupyter Notebooks application and locate the appropriate folder and file using Jupyter Notebooks. Many sub folders, like this one, contain the data in CSV files. You'll need that for the demonstrations, but in some cases, you'll need to follow the directions from the video to download the data and move it into the appropriate sub folder. I left that data for you to download in order to respect the licensing terms of the distributor.
If you don't have access to the exercise files, that's okay. You can still follow along with the video and watch how I use the files on the screen. Now, it's time to get going.
- Working with recommendation systems
- Evaluating similarity based on correlation
- Building a popularity-based recommender
- Classification-based recommendations
- Making a collaborative filtering system
- Content-based recommender systems
- Evaluating recommenders