Learn about regularization.
- [Instructor] In our recommendation system,…we take review data and, from there,…extract user attributes and movie attributes…to act as the model.…Using that model, we can make recommendations.…A common problem that can happen…when building a model like this is called overfitting.…Overfitting is when the model doesn't learn…the overall pattern of the data,…but instead picks up too much on specific data points.…Let's explain with an example.…Imagine that we have two movies.…The first movie's a horror comedy.…The second is a serious bloody horror movie…with no comedy at all.…
Both movies have horror elements,…but some viewers might prefer the funny movie…and other viewers might prefer the serious movie.…A good recommendation system will be able to…separate those two movies and see that,…while both have some similar elements,…they are very different movies that…appeal to different audiences.…A bad recommendation system that's overfitting…would focus entirely on the horror attribute…and disregard everything else.…That system would make worse recommendations,…
Author
Adam Geitgey
Released
4/10/2017Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.
- Building a machine learning system
- Training a machine learning system
- Refining the accuracy of the machine learning system
- Evaluating the recommendations received
Skill Level Intermediate
Duration
Views
-
Introduction
-
Welcome1m 1s
-
Set up environment2m 15s
-
-
1. The Basics of Making Recommendations
-
2. Ways of Making Recommendations
-
3. Getting to Know Our Tools
-
4. Building the Framework for Our Recommendation System
-
5. Collaborative Filtering with Matrix Factorization
-
6. Testing Our System
-
Use regularization1m 52s
-
7. Using the Recommendation System in a Real World Program
-
Find similar products1m 59s
-
Conclusion
-
Wrap up47s
-
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: Use regularization