Discover how to use Python to build programs that can make recommendations. This hands-on course explores different types of recommendation systems, and shows how to build each one.
- Hi, I'm Lillian Pierson. Welcome to the course. We'll be covering the solid essentials of building Recommendation Systems with Python. In this course we'll look at all the different types of recommendation methods there are and we'll practice building each type of recommendation system. I'll start by introducing you to the core concepts of recommendation systems then I'll be showing you how to build a popularity based recommender by using Python's Pandas library. Following that I'll show you how to recommend similar items based on correlation using Pandas.
Next you'll see how to use machine learning classification methods to make a Collaborative Filtering system by using the logistic progression model from scikit-learn library. After that I'll show you how to make a model based collaborative filtering system by using the Truncated SVD model also from scikit-learn. Then you'll see how to make a content based recommender by using the nearest neighbor approach. Lastly, I'll be showing you how to evaluate the completeness and precision of your models by using scikit-learn's classification metrics.
So in this course we'll be covering the popularity based recommender, both types of collaborative filtering systems, and content based recommenders plus some other tools and techniques. Now, let's get started.
- 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