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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-recommender-systems-for-business-applications/
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课程名称：为商业应用的实用推荐系统 课程概述： 本课程专注于使用Python构建实用推荐系统，帮助您了解大型科技公司（如亚马逊和Netflix）如何向用户推荐产品和服务。您将学习如何通过数据科学在数十亿美元的电子商务领域中利用推荐系统。课程包含如何使用真实数据实现自己的推荐系统，以及如何开发前沿分析和可视化工具以支持商业决策。此外，您还将学习如何应用机器学习和自然语言处理，根据用户的先前选择和/或用户档案进行推荐。 通过增强公司价值，提取来自零售和电子商务空间常见结构化和非结构化数据的可操作洞察，您将在数据分析技能上获得竞争优势。 课程特点： - 实践导向的培训，针对真实的推荐问题 - 使用Python数据科学技术，从结构化数据和非结构化文本数据中提取信息和洞察 - 建立实用的推荐系统基础，解决实际零售和电子商务问题，如根据用户过去的购买记录或用户档案推荐产品 为什么选择本课程： 讲师具有牛津大学环境与地理学硕士学位及剑桥大学数据科学博士学位，拥有多年分析现实数据的经验和国际刊物的发表记录。课程将帮助您在强大的云端Python环境Google Colab中流畅地部署基于数据科学的商业智能解决方案。 课程内容包括： - Google Colab中实施Python数据科学框架的主要方面 - 推荐系统的定义及其在零售领域的重要性 - 构建推荐系统所需的数据科学原则 - 利用可视化技术从数据中提取洞察 - 在Python中实现不同的推荐系统 - 使用自然语言处理(NLP)技术，根据描述或标题建议产品和服务 - 处理与在线零售产品描述、电影评分、书籍评分和描述相关的实际小案例研究 此外，您将获得讲师的持续支持，确保您从投资中获得最大价值。马上报名，开始您的数据科学之旅！
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH PYTHONAre 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 Python 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 Python 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 Python 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 BI solutions using a powerful clouded based python environment called GoogleColab. Specifically, you will Learn the main aspects of implementing a Python data science framework within Google ColabLearn 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 PythonUse 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:)
