Machine Learning for Marketers
This course is part of Data Science for Marketing.
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 Data Science 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:
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What you'll learn
Apply supervised learning for strategic marketing decisions
Implement CART tree analysis for customer segmentation
Enhance prediction accuracy using advanced algorithms
Develop recommendation systems for personalized marketing
Use unsupervised learning for market pattern discovery
Skills you'll gain
This course includes:
2.2 Hours PreRecorded video
29 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
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There are 4 modules in this course
This comprehensive course focuses on applying machine learning techniques to marketing challenges. Students learn both supervised and unsupervised learning methods, including CART trees, random forests, and principal components analysis. The curriculum covers practical applications such as customer segmentation, campaign response prediction, and recommendation systems. Through hands-on exercises and case studies, participants develop skills in model evaluation, cross-validation, and implementing advanced machine learning algorithms for marketing decision-making.
Supervised Learning for Strategic Marketing
Module 1 · 3 Hours to complete
CART Tree Analysis
Module 2 · 5 Hours to complete
Improving the Accuracy of Predictions
Module 3 · 5 Hours to complete
Unsupervised Learning
Module 4 · 6 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Data Science for Marketing
Instructors
Expert in Cross-Functional Collaboration and Leadership Education
A.W. Lukens serves as a distinguished course developer and instructor at the University of Colorado System, specializing in cross-functional collaboration and leadership development. Their expertise is demonstrated through a comprehensive suite of courses on Coursera, focusing on both theoretical foundations and practical applications of collaborative leadership. Their flagship course "Facilitating and Leading Cross-Functional Collaboration" maintains high engagement rates and positive student feedback, teaching essential skills for effective team leadership and facilitation. The curriculum spans from fundamental concepts to advanced leadership techniques, including courses on machine learning applications in marketing and wilderness first aid. Their teaching approach emphasizes practical, hands-on learning experiences, requiring students to practice skills with real-world applications. Their course content is particularly valuable for both current leaders and those aspiring to leadership positions, with no prior experience required for entry-level courses. Through their diverse course offerings, they have established themselves as a leading voice in professional development education, combining traditional leadership principles with modern collaborative approaches
Marketing Analytics Instructor at the University of Colorado System
Tony Cox, Jr. is an instructor at the University of Colorado System, specializing in marketing analytics and data-driven decision-making. He teaches several courses on Coursera, including "Customer Data Analytics for Marketers," "Machine Learning for Marketers," and "Regression Modeling for Marketers." His courses focus on equipping marketers with essential analytical skills to enhance their understanding of customer behavior and improve marketing strategies through data analysis.With a strong emphasis on practical applications, Tony's teaching integrates statistical concepts and machine learning techniques tailored for marketing professionals. His work aims to empower students to make informed decisions based on customer data, ultimately driving better marketing outcomes and fostering a deeper understanding of market dynamics.
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Frequently asked questions
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