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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-programming-build-a-recommendation-engine-in-django/
课程评论：没有评论
Coursera 课程《Python 编程：使用 Django 构建推荐引擎》是一门实用的课程，旨在教授学员如何利用 Django 框架和协同过滤（Collaborative Filtering）这一机器学习技术来构建一个电影推荐系统。 **课程亮点：** * **实战项目：** 学员将使用真实的 MovieLens 数据集，该数据集常用于各种机器学习教程。 * **数据库集成：** 课程将指导学员将 CSV 格式的数据导入到 SQL 数据库中，并通过 Django 模型进行管理，突出 SQL 数据库相比 CSV 文件的强大之处。 * **背景任务处理：** 引入并讲解强大的后台任务处理工具 Celery，并结合 Django 实现，使学员能够理解如何安排和运行训练模型预测等后台任务，从而构建更具可扩展性的系统，满足成千上万用户的需求。 * **AJAX 体验（无 JavaScript）：** 利用 HTMX 实现动态内容更新，用户无需刷新页面即可进行电影评分、排序、加载等操作，体验如同“点赞”、“订阅”按钮的流畅交互，且无需编写 JavaScript。 **推荐引擎构成：** 该推荐引擎主要由三个部分组成： 1. **Web 进程：** 使用 Django 收集用户兴趣，并提供推荐结果。 2. **机器学习流程：** 从 Django 中提取数据，进行转换，并训练协同过滤模型。 3. **工作进程：** 利用 Celery 作为“粘合剂”，调度和运行训练好的模型预测，并更新 Django 中的用户推荐数据。 **先修要求：** 建议学员具备以下经验： * Python 3.6+ * Django 3.2+ * Celery 与 Django 的结合使用经验（或对相关技术有基本了解） 通过本课程，学员将能够构建一个功能完善、可扩展的推荐系统，精通 Django、Celery 和 HTMX 等现代 Web 开发技术，并将机器学习模型集成到实际应用中。
Build a recommendation engine using Django & a Machine Learning technique called Collaborative Filtering.Users will rate movies and the system will automatically recommend new ones. These recommendations will be done in batches (ie not in real time) to unlock a more scalable system for training and helping thousands and thousands of users.For this course, we'll use a real dataset called MovieLens; this dataset is downloaded in CSV and is used on all kinds of machine learning tutorials. What's special about this course is you'll load this dataset into a SQL database through a Django model. This alone might be worth watching the course as SQL databases are far more powerful than CSV files.To do the batch inference we implement the incredibly powerful background worker process called Celery. If you haven't used Celery before, this will be an eye opening experience and when you couple it with Django you have a truly powerful worker process that can run tasks in the background, run tasks on a schedule, or a combination of both. Tasks in Celery are simply Python functions with a special decorator.For rating movies, we'll be using HTMX. HTMX is a way to dynamically update content *without* reloading the page at all. I am sure you know the experience whenever you click "like" or "subscribe" , that's what HTMX gives us without the overhead of using 1 line of JavaScript. This course shows us a practical implementation of using HTMX not just for rating movies, but also sorting them, loading them, and doing much more. The recommendation engine in Django is really a collection of 3 parts:Web Process: Setup up Django to collect user's interest and provide recommendations once available.Machine Learning Pipeline: Extract data from Django, transform it, and train a Collaborative Filtering model.Worker Process: This is the glue. We'll use Celery to schedule/run the trained model predictions and update data for Django-related user recommendations.Recommended ExperiencePython 3.6+ (such as 30 Days of Python)Django 3.2+ (such as Your First Django Web Project or Try Django 3.2)Celery with Django (such as Time & Tasks 2 or this blog post)
