In this course, you will :
- Learn how to use item attributes to make recommendations.
- Make useful comparisons between items by using both categorical and text data. Create profiles to suggest new items to users based on their previous preferences.
- Find new items to recommend to users by connecting with others who have similar tastes.
- Learn how to make user-based and item-based recommendations, as well as when and where to use them.
- Use k-nearest neighbours models to tap into the collective wisdom of the crowd and predict how someone will rate an item they haven't yet seen.
- Learn how the scarcity of real-world datasets affects your recommendations.
- Investigate the value of latent features and use them to gain a better understanding of your data.
- Introduction to Recommendation Engines
- Content-Based Recommendations
- Collaborative Filtering
- Matrix Factorization and Validating Your Predictions