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所在平台: Udemy |
课程主页: https://www.udemy.com/course/recommendation-systems/
课程评论：没有评论
课程名称：字节大小的块：推荐系统 课程概述：本课程是我们超过20小时课程“从0到1：机器学习与自然语言处理”的一个子集，因此请不要同时注册这两个课程。该课程没有先决条件，具备一些本科数学基础知识会有帮助，但不是必需的。如果想要运行提供的源代码，懂Python基础知识会更为有利。课程由一位斯坦福大学教育的前谷歌员工和一位拥有印度理工学院及印度管理学院背景的前Flipkart主分析师教授。这一团队在量化交易、分析和电子商务领域拥有数十年的实践经验。 推荐引擎能够执行多种任务，其中最重要的是为用户找到最相关的产品。内容推荐算法是基于产品的内容（属性、描述、词语等）为用户找到相关产品的方式。而协同过滤则是一个通用术语，指用户可以相互帮助找到他们喜欢的产品。如今，这种方法是最受欢迎的推荐策略。邻域模型（也称为基于记忆的方法）依赖于找到与当前用户相似的用户。相似性可以通过多种方式测量，包括欧几里得距离、皮尔逊相关和余弦相似度等。 隐因子方法通过用户历史数据识别影响用户的隐藏因子，矩阵分解被用来寻找这些因子。该方法最初是由Netflix Prize获胜者使用并推广的，许多现代推荐系统包括Netflix都使用某种形式的矩阵分解。课程还包括Python中的推荐系统实践，使用著名的数据集Movielens进行电影评分分析，运用Pandas处理数据，同时学习如何使用SciPy和NumPy。 没有详细教学大纲。
Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-) Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. Recommendation Engines perform a variety of tasks - but the most important one is to find products that are most relevant to the user. Content based filtering finds products relevant to a user - based on the content of the product (attributes, description, words etc).Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to RecommendationsNeighborhood models - also known as Memory based approaches - rely on finding users similar to the active user. Similarity can be measured in many ways - Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization. Recommendation Systems in Python!Movielens is a famous dataset with movie ratings. Use Pandas to read and play around with the data.Also learn how to use Scipy and Numpy
