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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-ultimate-beginners-guide-to-python-recommender-systems/
课程评论：没有评论
Coursera 课程 **"Python 推荐系统终极入门指南"** 是一门教授人工智能热门领域——推荐系统知识的课程。 本课程将从理论和实践两方面深入讲解推荐系统的工作原理。学员可以学习到基于协同过滤技术的推荐算法，并通过用户-用户协同过滤和物品-物品协同过滤的实践来为电影推荐实现算法。课程将首先使用小型数据集进行数学计算的测试，随后将学员的技术应用于著名的 MovieLens 数据集，该数据集包含超过 10 万条数据。 课程的一大亮点在于，学员将 **从零开始** 使用 Python 实现推荐算法，并理解其背后的数学原理。课程结束后，学员还将学习如何使用 LibRecommender 和 Surprise 这两个现成的库。 本课程适合作为推荐系统领域的入门课程，即使是初学者，也能在完成本课程后，获得扎实的理论和实践基础，能够独立完成简单的推荐系统项目，并为后续学习更高级课程打下坚实基础。
Recommender systems are a hot topic in Artificial Intelligence and are widely used for a lot of companies. They are everywhere recommending movies, music, videos, products, services, and so on. For example, when you finish watching a movie on Netflix, other movies you might like are indicated for you. This is the classic example of a recommender system!In this course, you will learn in theory and practice how recommender systems work! You will implement an algorithm based on the collaborative filtering technique applied to movie recommendations (user-based filtering and item-based filtering). We are going to use a small dataset to test all mathematical calculations. Then, we will test our algorithm using the famous MovieLens dataset, which has more than 100.000 instances. At the end of the course (after implementing the algorithm from scratch), you will learn how to use two pre-built libraries: LibRecommender and Surprise!What makes this course unique is that you will implement step by step from scratch in Python, learning all mathematical calculations. This can be considered the first course on recommender systems, so, if you have never heard about how to implement them, at the end you will have all the theoretical and practical background to develop some simple projects and also take more advanced courses. See you in class!
