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所在平台: Coursera专项课程 课程类别: 计算机科学 大学或机构: CourseraNew |
课程主页: https://www.coursera.org/specializations/recommender-systems
课程评论：没有评论
**推荐系统专业化课程总结** 本专业课程旨在帮助学习者掌握推荐系统的基本技术，预测用户偏好。课程内容涵盖从非个性化推荐和项目关联推荐到基于内容的协作过滤技术，以及更高级的主题，如矩阵分解、混合机器学习方法等。此外，还介绍了用户与产品偏好空间的降维技术。 该专业课程主要面向希望在工作中应用协作过滤等技术的数据挖掘专家，以及希望提高对推荐系统理解的数据知识型营销专业人士。课程包括互动式电子表格练习，帮助学生掌握不同的算法，并提供荣誉轨道，允许学生使用LensKit开源工具包深入研究。 学习者完成该专业课程后，能够实施和评估推荐系统。顶峰项目将所学课程内容与实际的推荐系统设计与分析结合，帮助学生综合运用所学知识。 **课程列表：** 1. **推荐系统简介：非个性化与基于内容的推荐** - 由明尼苏达大学提供，作为推荐系统专业的第一门课程。 2. **最近邻协作过滤** - 由明尼苏达大学提供，学习个性化推荐的基本技术。 3. **推荐系统的评估与指标** - 由明尼苏达大学提供，学习如何评价推荐系统的有效性。 4. **矩阵分解与高级技术** - 由明尼苏达大学提供，学习多种矩阵分解和混合机器学习技术。 5. **推荐系统顶峰项目** - 由明尼苏达大学提供，将所学知识应用于实际推荐系统设计和分析项目。 该课程适合对推荐系统感兴趣的学习者，鼓励报名参与。同时，所有课程均可免费注册。
Course Link: https://www.coursera.org/learn/recommender-systems-introduction
Name:Introduction to Recommender Systems: Non-Personalized and Content-Based
Description:Offered by University of Minnesota. This course, which is designed to serve as the first course in the Recommender Systems specialization, ... Enroll for free.
Course Link: https://www.coursera.org/learn/collaborative-filtering
Name:Nearest Neighbor Collaborative Filtering
Description:Offered by University of Minnesota. In this course, you will learn the fundamental techniques for making personalized recommendations ... Enroll for free.
Course Link: https://www.coursera.org/learn/recommender-metrics
Name:Recommender Systems: Evaluation and Metrics
Description:Offered by University of Minnesota. In this course you will learn how to evaluate recommender systems. You will gain familiarity with ... Enroll for free.
Course Link: https://www.coursera.org/learn/matrix-factorization
Name:Matrix Factorization and Advanced Techniques
Description:Offered by University of Minnesota. In this course you will learn a variety of matrix factorization and hybrid machine learning techniques ... Enroll for free.
Course Link: https://www.coursera.org/learn/recommeder-systems-capstone
Name:Recommender Systems Capstone
Description:Offered by University of Minnesota. This capstone project course for the Recommender Systems Specialization brings together everything ... Enroll for free.
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
推荐人系统专业化：推荐人系统是一个旨在预测用户偏好的过程。本专业涵盖了推荐系统中的所有基本技术，从非个性化和项目关联推荐器到基于内容的协作过滤技术，以及高级主题，例如矩阵分解，推荐系统的混合机器学习方法和降维技术用户产品偏好空间。 该专长旨在为希望在工作中实施协作过滤等技术的数据挖掘专家以及希望对这些主题更加熟悉的数据知识型营销专家提供服务。 这些课程提供基于电子表格的交互式练习，以掌握不同的算法，并提供荣誉轨道，您可以在这里使用LensKit开源工具包进行更深入的研究。 在本专业课程结束时，您将能够实施和评估推荐系统。顶峰项目将课程资料与现实的推荐者设计和分析项目结合在一起。
