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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/recommendation-models-gcp
课程评论：没有评论
课程名称：在 Google Cloud 上使用 TensorFlow 构建推荐系统 课程概述：在本课程中，您将运用分类模型和嵌入的知识，构建一个作为推荐引擎运作的机器学习管道。 • 设计基于内容的推荐引擎 • 实现协同过滤推荐引擎 • 构建用户和内容嵌入的混合推荐引擎 课程大纲： 1. 欢迎来到 Google Cloud 上的推荐系统 - 简介：本模块预览了课程中涉及的主题。 2. 推荐系统概况 - 简介：本模块定义推荐系统，回顾不同类型的推荐系统，并讨论开发推荐系统时常见的问题。 3. 基于内容的推荐系统 - 简介：本模块演示如何使用用户和项目的特征构建推荐系统，并说明如何使用 Qwiklabs 在 Google Cloud 上完成每一个实验。 4. 协同过滤推荐系统 - 简介：本模块展示如何结合来自不同用户的用户与项目互动数据，以提高预测的质量。 5. 用于推荐系统的神经网络 - 简介：本模块演示如何将各种推荐系统结合在一起，形成一种混合方法。 6. 强化学习 - 简介：本模块介绍强化学习的目标，并展示强化学习在机器学习中的作用。 7. 总结 - 简介：本模块回顾了本课程探讨的主题。 通过注册本课程，您同意 Qwiklabs 服务条款，详细信息请参见常见问题解答以及：https://qwiklabs.com/terms_of_service
Name:Welcome to Recommendation Systems on Google Cloud
Description:This module previews the topics covered in the course.
Name:Recommendation Systems Overview
Description:This module defines what recommendation systems are, reviews the different types of recommendation systems, and discusses common problems that arise when developing recommendation systems.
Name:Content-Based Recommendation Systems
Description:This module demonstrates how to build a recommendation system using characteristics of the users and items and how to use Qwiklabs to complete each of your labs using Google Cloud.
Name:Collaborative Filtering Recommendations Systems
Description:This module shows how the data of the interactions between users and items from many different users can be combined to improve the quality of predictions.
Name:Neural Networks for Recommendation Systems
Description:This module shows how various recommendation systems can be combined as part of a hybrid approach.
Name:Reinforcement Learning
Description:This module presents the goals of reinforcement learning and shows where reinforcement learning fits in machine learning.
Name:Summary
Description:This module reviews the topics explored in this course.
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation engine • Build a hybrid recommendation engine with user and content embeddings >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
