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.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder.
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
显示有关单元内容的信息
3个视频•总计55分钟
Text Classification Lecture 1•24分钟
Text Classification Lecture 2•11分钟
Text Classification Lecture 5 (Repeated in Week 3)•19分钟
6篇阅读材料•总计221分钟
Course Updates and Accessibility Support•1分钟
Earn Academic Credit for your Work!•10分钟
Course Support•10分钟
Introduction to using Google Colab for this course•10分钟
Python Syntax Review•10分钟
Python Basics & Colab Intro Reading•180分钟
2个编程作业•总计360分钟
Python Assessment 1: File I/O•180分钟
Python Assessment 2: Data Structures and Strings•180分钟
1个讨论话题•总计10分钟
Introduce Yourself!•10分钟
Neural Networks and Deep Learning
第 2 单元•小时 后完成
单元详情
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
显示有关单元内容的信息
2个视频•总计39分钟
Text Classification Lecture 3•15分钟
Text Classification Lecture 4•24分钟
2篇阅读材料•总计20分钟
An Example Codebook from Dr. Vargo•10分钟
An Example Paper from Dr. Vargo •10分钟
1个作业•总计180分钟
Supervised Text Classification•180分钟
Getting Started with Google Colab and Deep Learning
第 3 单元•小时 后完成
单元详情
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
显示有关单元内容的信息
2个视频•总计67分钟
Text Classification Lecture 5•19分钟
Text Classification Lecture 6•48分钟
2篇阅读材料•总计20分钟
Lecture Notebook Links •10分钟
Coding Lab 1: Data Preparation with Pandas•10分钟
1个作业•总计30分钟
Lab 1 Quiz•30分钟
Linear Models and Classification Metrics
第 4 单元•小时 后完成
单元详情
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
显示有关单元内容的信息
2个视频•总计17分钟
Text Classification Lecture 7•9分钟
Text Classification Lecture 8•9分钟
2篇阅读材料•总计20分钟
Lecture Notebook Links•10分钟
Coding Lab 2: Building a Model with K-Train•10分钟
1个作业•总计30分钟
Lab 2 Quiz•30分钟
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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。
CU Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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