Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment. Files are stored in Google Drive. This course can be taken for academic credit as part of CU Boulder鈥檚 Master of Science in Data Science (MS-DS) degree offered on the 糖心vlog官网观看 platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder鈥檚 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 /degrees/master-of-science-data-science-boulder.
Topic Modeling Lecture 1
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Skills You'll Learn
Topic Model, Data Structure, Unsupervised Text Classification, Machine Learning, Python Programming
From the lesson
What is topic modeling?
In this module, we will cover the fundamental concepts of topic modeling, also known as unsupervised machine learning on unstructured text documents. We will contrast unsupervised methods to supervised ones and survey common applications of topic modeling.
Taught By
Chris J. Vargo
Associate Professor
Scott Bradley
Senior Engineer