- Level Foundation
- المدة 12 ساعات hours
- الطبع بواسطة University of Colorado Boulder
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Offered by
عن
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.الوحدات
Introduction
1
Discussions
- Introduce Yourself!
1
Videos
- Specialization Introduction
2
Readings
- Earn Academic Credit for your Work!
- Course Support
Lectures
3
Videos
- Text Classification Lecture 1
- Text Classification Lecture 2
- Text Classification Lecture 5 (Repeated in Week 3)
Reference Documents
2
Readings
- Introduction to using Google Colab for this course
- Python Syntax Review
Assessments
- Python Assessment 1: File I/O
- Python Assessment 2: Data Structures and Strings
Lectures
2
Videos
- Text Classification Lecture 3
- Text Classification Lecture 4
Reference Documents
2
Readings
- An Example Codebook from Dr. Vargo
- An Example Paper from Dr. Vargo
Assessments
1
Quiz
- Supervised Text Classification
Lectures
2
Videos
- Text Classification Lecture 5
- Text Classification Lecture 6
Lecture Notebooks
1
Readings
- Lecture Notebook Links
Assessments
1
Readings
- Coding Lab 1: Data Preparation with Pandas
1
Quiz
- Lab 1 Quiz
Lectures
2
Videos
- Text Classification Lecture 7
- Text Classification Lecture 8
Lecture Notebooks
1
Readings
- Lecture Notebook Links
Assessments
1
Readings
- Coding Lab 2: Building a Model with K-Train
1
Quiz
- Lab 2 Quiz
Auto Summary
Elevate your marketing analytics skills with the "Supervised Text Classification for Marketing Analytics" course, specially designed for the Big Data and Analytics domain. This foundational course, curated by expert instructors from Coursera, focuses on harnessing the power of supervised deep learning to classify and label substantial marketing datasets efficiently. Dive into a comprehensive learning journey that begins with a conceptual overview of supervised machine learning. Progress through hands-on tutorials using real-world datasets and Python, guided by experienced instructors. The course culminates in a major project, allowing you to apply your newly acquired skills in a practical scenario. Ideal for those pursuing an academic credit, this course is part of CU Boulder’s prestigious Master of Science in Data Science (MS-DS) degree. This interdisciplinary program combines expertise from Applied Mathematics, Computer Science, Information Science, and more, and offers performance-based admissions with no application process. Whether you come from a background in computer science, information science, mathematics, or statistics, this program is tailored to enhance your proficiency in data science. With a total duration of 720 minutes, the course is available under the Starter subscription plan, making it accessible for learners aiming to build a strong foundation in marketing analytics. Join now and transform your ability to handle and analyze vast marketing data effectively. Explore further details about the MS-DS program at [MS-DS Program](https://www.coursera.org/degrees/master-of-science-data-science-boulder).

Chris J. Vargo

Scott Bradley