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This course is part of Text Marketing Analytics Specialization
Instructors: Chris J. Vargo
1,914 already enrolled
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(14 reviews)
Recommended experience
Beginner level
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
(14 reviews)
Recommended experience
Beginner level
Basic proficiency in Python including basic Python logic and data structures, Python’s built-in functions, and Python package pandas
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
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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://www.coursera.org/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.
4 videos4 readings2 programming assignments1 discussion prompt
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.
2 videos2 readings1 assignment
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.
2 videos2 readings1 assignment
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.
2 videos2 readings1 assignment
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.
University of Colorado Boulder
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University of Colorado Boulder
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O.P. Jindal Global University
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University of Colorado Boulder
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This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
University of Colorado Boulder
Degree · 2 years
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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