Understand how matrix completion is a good model for predicting user ratings.
- [Narrator] Let's take a look at create…review matrix as csv.py.…This code will generate a csv representation…of the review matrix that we can open in a spreadsheet.…First we load the data using Panda's read_csv function.…Then we use Panda's pivot_table function…to create the review matrix.…Finally, we'll use Panda's to_csv function…to save the result as a csv file.…Let's run the code.…Right click and choose run.…Great.…Now let's open that file in our spreadsheet application.…I'm using Numbers but any…spreadsheet application should work.…
This is our review matrix.…There's one row for each user and one column for each movie.…Each number represents a review entered by a user.…Blank spaces represent movies that have…not yet been reviewed by a user.…Imagine if we could figure out a way to fill…in all the blank spaces based on the numbers we know.…For example, let's look at user number three.…We can see that user number three gave four stars…to movie one and movie two, and five stars…to movie number three.…What if we could use the ratings we know…
Author
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
Related Courses
-
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
-
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.
CancelTake notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press 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: Recommend by predicting missing user ratings