Supervised Learning and Its Applications in Marketing
Master Python-based machine learning for marketing analytics. Learn classification, regression, and practical applications in customer analysis.
Master Python-based machine learning for marketing analytics. Learn classification, regression, and practical applications in customer analysis.
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.
1,127 already enrolled
Instructors:
English
Tiếng Việt
Powered by
What you'll learn
Apply Python for supervised learning in marketing
Develop classification and regression models
Build product recommendation systems
Predict customer lifetime value
Implement customer churn prediction
Analyze marketing campaign effectiveness
Skills you'll gain
This course includes:
4 Hours PreRecorded video
36 quizzes
Access on Mobile, Desktop, Tablet
FullTime access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 12 modules in this course
This comprehensive course explores supervised learning techniques and their practical applications in marketing. Students learn to develop and train machine learning models for classification and regression tasks using Python. The curriculum covers essential topics including customer behavior analysis, product recommendation systems, customer lifetime value prediction, and churn analysis. Through hands-on exercises and real-world examples, learners master the implementation of various algorithms from decision trees to artificial neural networks, gaining practical skills in deploying machine learning solutions for marketing challenges.
Introduction to Supervised Learning in Marketing
Module 1 · 2 Hours to complete
Getting Started With Supervised Learning in Marketing
Module 2 · 2 Hours to complete
Weekly Summative Assessment: Supervised Learning in Marketing
Module 3 · 1 Hours to complete
Deriving Insights from Data
Module 4 · 2 Hours to complete
Product Recommender System
Module 5 · 2 Hours to complete
Weekly Summative Assessment: Deriving Insights
Module 6 · 1 Hours to complete
Personalized Marketing
Module 7 · 2 Hours to complete
Customer Lifetime Value
Module 8 · 2 Hours to complete
Weekly Summative Assessment: Personalized Marketing
Module 9 · 1 Hours to complete
Retaining Customers
Module 10 · 1 Hours to complete
Deployment of Supervised Learning Models
Module 11 · 2 Hours to complete
Weekly Summative Assessment: Retaining Customers
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.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.