深受数千家公司学习者的喜爱
培训2人或更多？
试用DataCamp for Business课程描述
先决条件
Supervised Learning with scikit-learn1
Introduction to Recommendation Engines
What problems are recommendation engines designed to solve and what data are best suited for them? Discern what insightful recommendations can be made even with limited data, and learn how to create your own recommendations.
2
Content-Based Recommendations
Discover how item attributes can be used to make recommendations. Create valuable comparisons between items with both categorical and text data. Generate profiles to recommend new items for users based on their past preferences.
3
Collaborative Filtering
Discover new items to recommend to users by finding others with similar tastes. Learn to make user-based and item-based recommendations—and in what context they should be used. Use k-nearest neighbors models to leverage the wisdom of the crowd and predict how someone might rate an item they haven’t yet encountered.
4
Matrix Factorization and Validating Your Predictions
Understand how the sparsity of real-world datasets can impact your recommendations. Leverage the power of matrix factorization to deal with this sparsity. Explore the value of latent features and use them to better understand your data. Finally, put the models you’ve discovered to the test by learning how to validate each of the approaches you’ve learned.
Building Recommendation Engines in Python
课程完成 通过 DataCamp for Mobile 提升您的数据技能
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。