Recommender Systems 专项课程
Master Recommender Systems。Learn to design, build, and evaluate recommender systems for commerce and content.
本专项课程介绍
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.
制作方：
5 courses
按照建议的顺序或选择您自己的顺序。
项目
旨在帮助您实践和应用所学到的技能。
证书
在您的简历和领英中展示您的新技能。
课程
- Intermediate Specialization.
- Some related experience required.
第 1 门课程
Introduction to Recommender Systems: Non-Personalized and Content-Based
- 课程学习时间
- 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
- 字幕
- English
课程概述
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using su您可以选择只参加本课程。 了解更多。
第 2 门课程
Nearest Neighbor Collaborative Filtering
- 字幕
- English
课程概述
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar t您可以选择只参加本课程。 了解更多。
第 3 门课程
Recommender Systems: Evaluation and Metrics
- 字幕
- English
课程概述
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, pr您可以选择只参加本课程。 了解更多。
第 4 门课程
Matrix Factorization and Advanced Techniques
- 字幕
- English
课程概述
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recomm您可以选择只参加本课程。 了解更多。
第 5 门课程
Recommender Systems Capstone
计划开课班次：Dec 24- 课程学习时间
- 1-3 weeks of study, 3-5 hours per week
- 字幕
- English
毕业项目介绍
This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case您可以选择只参加本课程。 了解更多。
制作方
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand
Assistant Professor
FAQs
More questions? Visit the Learner Help Center.
