Practical Recommender Systems For Business Applications in R

使用 R 为业务应用程序实现数据科学驱动的推荐系统

类型：电子学习 | MP4 | 视频：h264, 1280×720 | 音频：AAC，44.1 KHz
语言：英语 | 大小：1.90 GB | 时长：36 节课 • 3h 19m

你会学到什么

• 了解什么是推荐系统及其对商业智能的重要性
• 学习在 R 编程语言中实现数据科学技术的主要方面
• 使用 R 编程语言实现实用的推荐系统
• 了解推荐系统的理论和实践方面

要求

• 能够在计算机上操作和安装软件
• 事先接触 R 编程概念会很有帮助
• 之前接触过 R Studio 环境
• 对学习实用推荐系统的兴趣

描述

报名参加我的最新课程，了解如何使用 R 构建实用推荐系统

您是否有兴趣了解亚马逊和 Netflix 等大型科技巨头如何向您推荐产品和服务？

您想了解数据科学如何通过推荐系统入侵数十亿的电子商务空间吗？

您想使用真实数据实现自己的推荐系统吗？

您想开发尖端分析和可视化来支持业务决策吗？

您是否有兴趣部署机器学习和自然语言处理以根据先前的选择和/或用户配置文件提出建议？

如果您可以应用 R 数据分析技能来根据用户偏好做出数据驱动的建议，那么您就可以获得超越其他数据科学家的优势

通过从零售和电子商务领域中常见的常用结构化和非结构化数据中提取可操作的见解来提高公司或业务的价值

通过精通开发实用推荐系统的最重要支柱，从众多其他数据分析师中脱颖而出

我的课程是实际推荐相关问题的实践培训 – 您将学习使用重要的 R 数据科学技术从结构化数据（例如在典型零售和/或商业环境中获得的数据）和非结构化文本中获取信息和见解数据

我的课程为使用 Python 执行实际的、现实生活中的推荐系统任务奠定了基础。通过学习本课程，您将在数据科学之旅中迈出重要一步，成为部署 R 编程数据科学技术以回答实际零售和电子商务问题的专家（例如，根据他们以前的产品推荐什么样的产品）购买或他们的用户资料）。

你为什么要参加我的课程？

我拥有英国牛津大学的硕士学位（地理与环境）。我还在剑桥大学完成了数据科学密集型博士学位（热带生态与保护）。

我在分析来自不同来源的真实数据和为国际同行评审期刊制作出版物方面拥有多年的经验。

本课程将帮助您熟练地在 R 中部署基于数据科学的推荐系统，以便为业务决策提供信息。具体来说，您将

学习在 R 编程语言中实现数据科学技术的主要方面

了解什么是推荐系统以及为什么它们对零售空间如此重要

学习实施构建推荐系统所需的通用数据科学原则

使用可视化来支持您从结构化和非结构化数据中收集到的见解

用 R 编程语言实现不同的推荐系统

使用常见的自然语言处理 (NLP) 技术根据描述和/或标题推荐产品和服务

您将从事与 (a) 在线零售产品描述 (b) 电影评级 (c) 书籍评级和描述等相关的实用迷你案例研究

除上述所有内容外，您还将获得我的持续支持，以确保您从投资中获得最大价值！

本课程适用于谁

• 想要掌握用于数据科学的 R 编程语言的人
• 对开发强大的数据可视化感兴趣的学生
• 学习根据先前的选择做出产品和服务推荐
• 为您的问题确定最佳推荐系统

Implementing Data Science Driven Recommender Systems For Business Applications With R

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.90 GB | Duration: 36 lectures • 3h 19m

What you’ll learn

• Learn what recommender systems are and their importance for business intelligence
• Learn the main aspects of implementing data science technique within the R Programming Language
• Implement practical recommender systems using R Programming Language
• Learn about the theoretical and practical aspects of recommender systems

Requirements

• Be Able To Operate & Install Software On A Computer
• Prior Exposure to R Programming Concepts Will be Helpful
• Prior Exposure to the R Studio Environment
• An Interest in Learning About Practical Recommender Systems

Description

ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH R

Are 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 Preferences

By 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 systems

MY 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 data

My 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 Language

Learn what recommender systems are and why they are so vital to the retail space

Learn to implement the common data science principles needed for building recommender systems

Use visualisations to underpin your glean insights from structured and unstructured data

Implement different recommender systems in the R Programming Language

Use common natural language processing (NLP) techniques to recommend products and services based on descriptions and/or titles

You 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 few

In 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 ?

Who this course is for

• People Wanting To Master The R Programming Language For Data Science
• Students Interested In Developing Powerful Data Visualisations
• Learning to Make Product and Service Recommendations Based on Prior Choices
• Identify the Best Recommender System For Your Problem