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.

About this Course
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
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Try Coursera for BusinessWhat you will 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 will gain
- Topic Model
- Machine Learning
- Python Programming
- Unsupervised Text Classification
- Data Structure
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
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Start working towards your Master's degree
Syllabus - What you will learn from this course
What is topic modeling?
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
About the Text Marketing Analytics Specialization

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