Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.
Supervised Text Classification for Marketing Analytics
Supervised Text Classification for Marketing Analytics
本课程是 Text Marketing Analytics 专项课程 的一部分
位教师：Chris J. Vargo
访问权限由 Coursera Learning Team 提供
2,247 人已注册
您将学到什么
Describe text classification and related terminology (e.g., supervised machine learning)
Apply text classification to marketing data through a peer-graded project
Apply text classification to a variety of popular marketing use cases via structured homeworks
Train, evaluate and improve the performance of the text classification models you create for your final project
您将获得的技能
- Tensorflow
- Artificial Neural Networks
- Marketing Analytics
- Deep Learning
- Data Manipulation
- Python Programming
- Supervised Learning
- Classification Algorithms
- Feature Engineering
- Text Mining
- Scikit Learn (Machine Learning Library)
- Performance Metric
- Transfer Learning
- Predictive Modeling
- Google Cloud Platform
- Machine Learning
- Model Evaluation
- 技能部分已折叠。显示 10 项技能，共 17 项。
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
3 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
积累特定领域的专业知识
本课程是 Text Marketing Analytics 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4个模块
In this module, we will learn about the different types of machine learning that exist and the operational steps of building a supervised machine learning model. We will also cover performance metrics of text classification.
涵盖的内容
3个视频6篇阅读材料2个编程作业1个讨论话题
In this module, we will learn about neural networks and supervised machine learning. Then we will dive into real supervised machine learning projects and the key decisions that need to be made when conducting one's own project.
涵盖的内容
2个视频2篇阅读材料1个作业
In this module, we will learn how to work in the Google Colab and Google Drive environment. We will get started with supervised learning by using a wrapper for Google’s Tensorflow and transformer models.
涵盖的内容
2个视频2篇阅读材料1个作业
In this module, we will learn how to workshop a variety of supervised machine learning models that rely on linear-based models. We will also learn how to perform an external performance analysis of models in sci-kit learn.
涵盖的内容
2个视频2篇阅读材料1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。
攻读学位
课程 是 University of Colorado Boulder提供的以下学位课程的一部分。如果您被录取并注册，您已完成的课程可计入您的学位学习，您的学习进度也可随之转移。
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
从 Data Science 浏览更多内容
University of Colorado Boulder
University of Colorado Boulder
O.P. Jindal Global University
O.P. Jindal Global University