Discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations.
In this hands-on course, Lillian Pierson, P.E. covers the different types of recommendation systems out there, and shows how to build each one. She helps you learn the concepts behind how recommendation systems work by taking you through a series of examples and exercises.
Once you’re familiar with the underlying concepts, Lillian explains how to apply statistical and machine learning methods to construct your own recommenders. She demonstrates how to build a popularity-based recommender using the Pandas library, how to recommend similar items based on correlation, and how to deploy various machine learning algorithms to make recommendations. At the end of the course, she shows how to evaluate which recommender performed the best.
Earn a sharable certificate
Share what you’ve learned, and be a standout professional in your desired industry with a certificate showcasing your knowledge gained from the course.
- Showcase on your LinkedIn profile under “Licenses and Certificate” section
- Download or print out as PDF to share with others
- Share as image online to demonstrate your skill