Get ideas on other applications for recommendations.
- [Adam] In this course, we've covered the essential skills you need to know how to build a recommendation system. I encourage you to try out your new skills with your own data. Go out and build something. If you don't have any of your own product data to work with, you can download real life data sets on the web. One data set you can try out is the no-cost MovieLens Dataset. The MovieLens Dataset contains tens of millions of movie reviews which will let you experiment with adapting what you've learned on a much larger scale. If you are comfortable coding in Java, you can also experiment with Apache Mahout. Apache Mahout is an open source application that implements the same kind of collaborative filtering algorithm as we learned about in this course.
Apache Mahout is designed to run across multiple computers which can be very useful for large data sets that are too slow to factor on a single machine. Thanks for watching, feel free to connect with me online.
Recommendation 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