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所在平台: Udemy |
课程主页: https://www.udemy.com/course/recommendation-systems-with-terraform-on-google-cloud/
课程评论：没有评论
课程名称：在谷歌云上使用Terraform构建推荐系统 课程概述： 本课程旨在利用谷歌云计算的强大功能，帮助您构建先进的推荐系统，从而解锁个性化用户体验的潜力并驱动参与度。您将学习如何设计、部署和管理强大的推荐引擎，以满足现代应用程序的需求，并结合Terraform提供的自动化和可扩展性。 学习内容： 1. **GCP生态系统介绍**：了解与推荐系统相关的GCP核心组件，包括计算引擎、云存储、大数据分析和Vertex AI。 2. **基本统计概念**：掌握基本的统计技术，如主成分分析（PCA），这些都是理解和实施推荐算法的重要基础。 3. **常见的推荐系统**：探索多种流行的推荐方法，包括协同过滤、基于内容的过滤以及混合模型。 4. **基于过滤的推荐系统**：深入了解基于过滤的推荐系统的机制，理解用户-项目交互如何生成个性化建议。 5. **其他推荐系统**：发现更多推荐技术，如基于知识和基于会话的系统，扩展您的工具包以应对多样化的应用场景。 6. **Terraform入门**：学习Terraform的基本知识这一强大的基础设施即代码工具，应用于自动化在GCP上推荐系统的部署和管理。 7. **推荐的文本分析**：了解文本分析技术（如自然语言处理）及如何将其整合到推荐系统中，以利用文本数据改善推荐效果。 适合对象： - 有意构建和部署推荐系统的数据科学家和机器学习工程师。 - 希望在GCP上为推荐引擎自动化基础设施配置的软件开发人员和DevOps专业人员。 - 希望了解推荐系统技术方面的商业分析师和产品经理，以做出明智决策。 先决条件： - 基本的Python编程理解。 - 对机器学习概念的熟悉将有助益，但不是必需的。 通过本课程，您将能够： - 自信地设计和实施各种推荐系统算法。 - 利用GCP的基础设施和机器学习服务构建可扩展的推荐引擎。 - 使用Terraform自动化推荐系统的部署和管理。 - 引入文本分析技术以增强推荐的个性化效果。 现在就注册，开始您在谷歌云上构建最前沿推荐系统的旅程吧！
Recommendation Systems With Terraform On Google Cloud: Use the Power of Google Cloud Computing To Build State-of-the-Art Recommender SystemsUnlock the potential of personalized user experiences and drive engagement with this comprehensive course on building state-of-the-art recommender systems using Google Cloud Platform (GCP) and Terraform.Course OverviewThis course will equip you with the knowledge and tools to design, deploy, and manage powerful recommendation engines that can be scaled to meet the demands of modern applications. You'll learn how to leverage the vast capabilities of GCP's infrastructure and machine learning services, combined with the automation and scalability offered by Terraform.What You'll LearnIntroduction to the GCP Ecosystem: Learn about the core components of GCP relevant to recommender systems, including Compute Engine, Cloud Storage, BigQuery, and Vertex AI.Essential Statistical Concepts: Master fundamental statistical techniques, such as Principal Component Analysis (PCA), which are crucial for understanding and implementing recommender algorithms.Common Recommender Systems: Explore a variety of popular recommendation approaches, including collaborative filtering, content-based filtering, and hybrid models.Filtering-Based Recommender Systems: Dive deep into the mechanics of filtering-based recommenders, understanding how they leverage user-item interactions to generate personalized suggestions.Other Recommender Systems: Discover additional recommendation techniques, such as knowledge-based and session-based systems, expanding your toolkit for diverse scenarios.Getting Started with Terraform: Learn the basics of Terraform, a powerful infrastructure-as-code tool, and apply it to automate the deployment and management of your recommender systems on GCP.Text Analysis for Recommendations: Gain insights into text analysis techniques (e.g., NLP) and how they can be integrated into recommender systems to leverage textual data for improved recommendations.Who This Course Is ForThis course is designed for:Data scientists and machine learning engineers interested in building and deploying recommender systems.Software developers and DevOps professionals seek to automate infrastructure provisioning for recommendation engines on GCP.Business analysts and product managers who want to understand the technical aspects of recommender systems to make informed decisions.PrerequisitesBasic understanding of Python programming.Familiarity with machine learning concepts is beneficial but not required.By the end of this course, you will be able to:Confidently designed and implemented various recommender system algorithms.Leverage GCP's infrastructure and machine learning services for scalable recommendation engines.Automate the deployment and management of recommender systems using Terraform.Incorporate text analysis techniques to enhance the personalization of recommendations.Enrol now and start your journey toward building cutting-edge recommender systems on Google Cloud!
