Ementorhub

What you'll learn

Describe the concept of topic modeling and related terminology (e.g., unsupervised machine learning)

Apply topic modeling to marketing data via a peer-graded project

Apply topic modeling to a variety of popular marketing use cases via homework assignments

Evaluate, tune and improve the performance the topic model you create for your project

Skills you'll gain

Data Science

Unstructured Data

Machine Learning

Deep Learning

Text Mining

Data Processing

Unsupervised Learning

Natural Language Processing

Marketing Analytics

JSON

Machine Learning Methods

There are 5 modules in this course

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’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.

The Assumptions of a Topic Model, Bag of Words, and Natural Language Processing

Prepping Amazon Review Data

Pre-Processing Text and Training a Topic Model

Topic Modeling Evaluation, Classification, and Neural Network Approaches