Unsupervised Learning and Its Applications in Marketing
Master Python-based unsupervised learning techniques for marketing analytics, including clustering, dimensionality reduction, and customer segmentation.
Master Python-based unsupervised learning techniques for marketing analytics, including clustering, dimensionality reduction, and customer segmentation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning for Marketing Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
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What you'll learn
Apply Python for implementing unsupervised learning algorithms
Master clustering techniques for customer segmentation
Implement dimensionality reduction methods
Develop anomaly detection systems
Create market basket analysis solutions
Skills you'll gain
This course includes:
4.6 Hours PreRecorded video
36 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 12 modules in this course
This comprehensive course explores unsupervised machine learning techniques and their applications in marketing. Students learn to implement various algorithms including clustering, dimensionality reduction, and association rule mining using Python. The curriculum covers practical applications such as customer segmentation, market basket analysis, and anomaly detection, with hands-on exercises and real-world examples to develop skills in data-driven marketing decision making.
Fundamentals of Unsupervised Learning
Module 1 · 2 Hours to complete
Clustering and Its Types
Module 2 · 1 Hours to complete
Weekly Summative Assessment: Fundamentals of Unsupervised Learning and Clustering
Module 3 · 1 Hours to complete
Data-Driven Customer Segmentation
Module 4 · 2 Hours to complete
Dimensionality Reduction
Module 5 · 2 Hours to complete
Weekly Summative Assessment: Data-Driven Customer Segmentation and Dimensionality Reduction
Module 6 · 1 Hours to complete
Anomaly Detection
Module 7 · 2 Hours to complete
Autoencoders and Association Learning
Module 8 · 2 Hours to complete
Weekly Summative Assessment: Anomaly Detection, Autoencoders, and Association Learning
Module 9 · 1 Hours to complete
Semi-Supervised Learning
Module 10 · 2 Hours to complete
Recommender systems Using RBM
Module 11 · 2 Hours to complete
Weekly Summative Assessment: Semi-Supervised Learning and Recommender systems Using RBM
Module 12 · 1 Hours to complete
Fee Structure
Instructor
Emerging Scholar in Social Media Analytics and IT at IIM Lucknow.
Prof. Ambica Ghai is currently pursuing her PhD in Management with a specialization in IT and Systems at IIM Lucknow. Her research focuses on social media listening and monitoring, particularly examining the interplay between textual data and image analytics. This involves analyzing how images and text together can provide insights into consumer behavior and preferences.
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