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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-recommender-systems-for-business-applications-in-r/
课程评论：没有评论
课程名称：实用推荐系统在商业应用中的R实践 课程概述： 您是否对科技巨头如亚马逊和Netflix如何向您推荐产品和服务感兴趣？您想学习数据科学如何通过推荐系统改变数十亿美元的电子商务领域吗？本课程将指导您实现自己的推荐系统，通过真实的数据进行实践，以便您能够开发前沿的分析和可视化工具，支持商业决策。 通过本课程，您将学习如何利用R语言进行数据分析，以便根据用户偏好做出数据驱动的推荐，进而提升您所在公司的价值。课程将强调提取结构化和非结构化数据中的可操作洞察力，以帮助您在零售和电子商务领域脱颖而出。 课程特点： 1. 实践导向：通过处理真实的推荐相关问题，您将在R中学习重要的数据科学技术，分析零售和商业环境中的结构化数据和非结构化文本数据。 2. 内容丰富：课程将帮助您了解推荐系统的主要方面及其在零售业务中的重要性，学习实施常见的数据科学原则以构建推荐系统。 3. 可视化技能：您将学习如何使用可视化技术深入洞察结构化和非结构化数据。 4. 自然语言处理：掌握常见的自然语言处理技术，通过描述和标题推荐产品和服务。 5. 实践案例：您将参与与在线零售产品描述、电影评分、书籍评分等相关的迷你案例研究。 课程由拥有牛津大学人文地理与环境学硕士学位以及剑桥大学的密集型数据科学博士学位的讲师授课，讲师在真实数据分析与国际期刊发表方面拥有丰富经验。选修本课程，您将获得持续支持，确保您从学习中获得最大的价值。 现在就报名加入吧！
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH RAre you interested in learning how the Big Tech giants like Amazon and Netflix recommend products and services to you?Do you want to learn how data science is hacking the multibillion e-commerce space through recommender systems?Do you want to implement your own recommender systems using real-life data?Do you want to develop cutting edge analytics and visualisations to support business decisions?Are you interested in deploying machine learning and natural language processing for making recommendations based on prior choices and/or user profiles?You Can Gain An Edge Over Other Data Scientists If You Can Apply R Data Analysis Skills For Making Data-Driven Recommendations Based On User PreferencesBy enhancing the value of your company or business through the extraction of actionable insights from commonly used structured and unstructured data commonly found in the retail and e-commerce space Stand out from a pool of other data analysts by gaining proficiency in the most important pillars of developing practical recommender systemsMY COURSE IS A HANDS-ON TRAINING WITH REAL RECOMMENDATION RELATED PROBLEMS- You will learn to use important R data science techniques to derive information and insights from both structured data (such as those obtained in typical retail and/or business context) and unstructured text dataMy course provides a foundation to carry out PRACTICAL, real-life recommender systems tasks using Python. By taking this course, you are taking an important step forward in your data science journey to become an expert in deploying the R Programming data science techniques for answering practical retail and e-commerce questions (e.g. what kind of products to recommend based on their previous purchases or their user profile).Why Should You Take My Course?I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation).I have several years of experience in analyzing real-life data from different sources and producing publications for international peer-reviewed journals.This course will help you gain fluency in deploying data science-based recommended systems in R to inform business decisions. Specifically, you will Learn the main aspects of implementing data science techniques in the R Programming LanguageLearn what recommender systems are and why they are so vital to the retail spaceLearn to implement the common data science principles needed for building recommender systemsUse visualisations to underpin your glean insights from structured and unstructured dataImplement different recommender systems in the R Programming LanguageUse common natural language processing (NLP) techniques to recommend products and services based on descriptions and/or titlesYou will work on practical mini case studies relating to (a) Online retail product descriptions (b) Movie ratings (c) Book ratings and descriptions to name a fewIn addition to all the above, you'll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!ENROLL NOW:)
