Packt
Building Recommender Systems with Machine Learning and AI
Packt

Building Recommender Systems with Machine Learning and AI

本课程是 Recommender Systems 专项课程 的一部分

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

推荐体验

2 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

2 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Evaluate and optimize recommender system performance using metrics like RMSE and MAE.

  • Master content-based and collaborative filtering techniques to build personalized recommendation engines.

  • Implement and tune matrix factorization and deep learning methods for scalable recommendation systems.

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

7 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Recommender Systems 专项课程 专项课程的一部分
在注册此课程时,您还会同时注册此专项课程。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有14个模块

In this module, we will lay the foundation for the course by setting up the development environment with Anaconda, familiarizing you with the course materials, and introducing you to creating simple movie recommendations.

涵盖的内容

7个视频2篇阅读材料

In this module, we will cover the essentials of Python programming, including basic syntax, data structures, and functions. We will also delve into Boolean expressions and loops through hands-on challenges.

涵盖的内容

4个视频1个插件

In this module, we will explore various methods for evaluating recommender systems, including accuracy metrics, hit rates, and diversity measures. We will also review practical examples and quizzes to reinforce learning.

涵盖的内容

9个视频1个作业1个插件

In this module, we will focus on the architecture of a recommender engine framework, guiding you through code walkthroughs and activities to implement and test various recommendation algorithms.

涵盖的内容

4个视频1个插件

In this module, we will dive into content-based filtering methods, exploring metrics like cosine similarity and KNN. We will also conduct hands-on activities to produce and evaluate movie recommendations.

涵盖的内容

6个视频1个插件

In this module, we will cover neighborhood-based collaborative filtering techniques, including user-based and item-based methods. Practical exercises and activities will help solidify your understanding of these approaches.

涵盖的内容

13个视频1个作业1个插件

In this module, we will explore matrix factorization methods like PCA and SVD, demonstrating how to apply these techniques to movie rating datasets. We will also focus on improving these methods through hyperparameter tuning.

涵盖的内容

6个视频1个插件

In this module, we will provide an optional deep dive into deep learning, covering fundamental concepts, neural network architectures, and practical implementations using TensorFlow and Keras.

涵盖的内容

25个视频1个插件

In this module, we will focus on applying deep learning to recommender systems, exploring techniques like Restricted Boltzmann Machines (RBM) and auto-encoders. We will also cover practical evaluation and tuning methods.

涵盖的内容

19个视频1个作业1个插件

In this module, we will explore methods to scale up recommendation systems, including using Apache Spark for large-scale data processing and Amazon's DSSTNE and SageMaker for deploying scalable machine learning models.

涵盖的内容

11个视频1个插件

In this module, we will tackle real-world challenges faced by recommender systems, such as the cold start problem, filtering bubbles, and fraud. We will also explore solutions to these issues through practical exercises.

涵盖的内容

11个视频1个作业1个插件

In this module, we will study real-world case studies of YouTube and Netflix, focusing on their recommendation strategies and the use of deep learning and hybrid approaches to enhance recommendation quality.

涵盖的内容

4个视频1个插件

In this module, we will explore hybrid recommendation approaches, combining multiple algorithms to improve recommendation accuracy and diversity. Practical exercises will guide you through implementing and evaluating hybrid systems.

涵盖的内容

2个视频1个作业1个插件

In this module, we will wrap up the course by summarizing key points, providing resources for further study, and introducing advanced topics and emerging trends in recommender systems to keep you up-to-date.

涵盖的内容

1个视频1篇阅读材料2个作业

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Packt - Course Instructors
Packt
971 门课程229,122 名学生

提供方

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