Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content-based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.
* Actual course outline may vary depending on offering center. Contact your sales representative for more information.
This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project.
Working in a hands-on lab environment led by our expert instructor, attendees will:
Understand the different kinds of recommender systems
Master data-wrangling techniques using the pandas library
Building an IMDB Top 250 Clone
Build a content-based engine to recommend movies based on real movie metadata
Employ data-mining techniques used in building recommenders
Build industry-standard collaborative filters using powerful algorithms
Building Hybrid Recommenders that incorporate content-based and collaborative filtering