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A.W. Lukens,
Tony Cox, Jr.,
and
Ric Mills
"Machine Learning for Marketers" is an advanced course tailored for professionals looking to integrate machine learning into their marketing strategies. This course uniquely focuses on both predictive analytics and decision-making, using supervised learning methods to analyze and forecast customer behavior. Participants will learn to implement advanced machine learning techniques, enhancing the accuracy of predictions and informing better marketing decisions. The course also covers campaign analysis through rigorous testing methods like cross-validation, ensuring the reliability of marketing strategies. A key feature of this course is its coverage of unsupervised learning algorithms, enabling learners to uncover hidden patterns in marketing data for sophisticated customer segmentation and market analysis. Additionally, the course discusses optimizing product positioning using dimensionality reduction techniques and improving personalized customer experiences through recommender system technology.
Register for this course and see more details by visiting:
OpenCourser.com/course/jjrg8l/machine
What's inside
Syllabus
Supervised Learning for Strategic Marketing
Apply supervised learning to discover and visualize patterns in customer behaviors and refine your marketing strategies. This module equips you with the tools to improve predictive accuracy and tailor marketing efforts for maximum impact.
Read more
Syllabus
Supervised Learning for Strategic Marketing
Apply supervised learning to discover and visualize patterns in customer behaviors and refine your marketing strategies. This module equips you with the tools to improve predictive accuracy and tailor marketing efforts for maximum impact.
Read more
CART Tree Analysis
Transform your marketing approach with CART tree analysis. Learn to segment customers precisely and predict campaign responses, optimizing your marketing resources and strategies for better customer engagement and retention.
Improving the Accuracy of Predictions
Advance your predictive capabilities in marketing. This module teaches you to enhance model accuracy, tackle data imbalances, and select the most effective strategies, ensuring your marketing campaigns hit the mark every time.
Unsupervised Learning
Apply unsupervised learning to uncover hidden patterns in marketing data. Use techniques such as Principal Components Analysis for insightful segmentation and personalized marketing strategies that can elevate customer engagement and lifetime value (LTV).
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Investigates machine learning strategies for marketing, which is an active area of research and practice with high impact in the modern economy
Taught by instructors with relevant names in machine learning and marketing
Covers practical machine learning methods applied in marketing that help learners create predictive and decision-making models
Provides hands-on experience with real-world marketing scenarios and data using supervised and unsupervised learning
Requires background in machine learning and marketing concepts
Doesn't cover advanced topics such as natural language processing for marketing
Save this course
Save Machine Learning for Marketers to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning for Marketers with these
activities:
Enhance Your Statistical Analysis Skills
Show steps
Refresh your statistical analysis skills to strengthen your foundation for the advanced techniques covered in this course.
Browse courses on
Statistical Analysis
Show steps
-
Review materials on basic statistical concepts
-
Practice solving statistical problems
-
Ensure proficiency in using statistical software
Review Essential Statistics
Show steps
Bring statistics knowledge from a previous course up to speed for more successful learning in this program.
Browse courses on
Statistics
Show steps
-
Review notes from a previous course on statistics.
-
Complete online exercises or practice problems.
-
Take a practice quiz.
Review ML Basics
Show steps
Review essential machine learning fundamentals and brush up on supervised learning concepts
Browse courses on
Machine Learning Fundamentals
Show steps
-
Recall key concepts of supervised learning
-
Go through notes and materials from previous ML courses or tutorials
-
Complete practice questions or exercises
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Participate in Discussion Groups
Show steps
Engage in discussions with peers to clarify concepts, exchange ideas, and gain diverse perspectives on the topics covered in this course.
Show steps
-
Join a peer discussion group or forum
-
Actively participate in discussions related to course topics
-
Ask questions and provide insights to enhance understanding
Learn about Decision Trees
Show steps
To enhance your understanding of decision trees, explore guided tutorials focusing on their structure, algorithms, and applications in marketing.
Browse courses on
Decision Trees
Show steps
-
Follow tutorials on the concepts of decision trees
-
Explore examples of CART tree analysis
-
Practice implementing decision trees for marketing use cases
Join a Study Group
Show steps
Enhance your understanding through discussions and collaboration with peers
Browse courses on
Collaborative Learning
Show steps
-
Reach out to classmates or fellow learners
-
Organize regular study sessions
-
Discuss course materials, share insights, and work on assignments together
Attend Predictive Analytics Workshops
Show steps
To enhance your knowledge and practical skills, attend workshops focused on predictive analytics, gaining hands-on experience with techniques and tools used in marketing.
Show steps
-
Research and identify relevant predictive analytics workshops
-
Register and participate in the workshops
-
Engage with industry experts and practitioners
Study Group Discussions
Show steps
Enhance understanding through collaboration, knowledge sharing, and peer feedback.
Show steps
-
Join or create a study group with fellow learners.
-
Discuss course concepts and share insights.
-
Provide feedback and support to group members.
Practice Predictive Modeling
Show steps
Sharpen your predictive modeling skills through repetitive exercises
Browse courses on
Predictive Analytics
Show steps
-
Use online platforms or textbooks for practice problems
-
Participate in coding challenges or competitions
-
Analyze real-world datasets and make predictions
Supervised Learning Drills
Show steps
Gain proficiency in supervised learning techniques to enhance predictive accuracy.
Browse courses on
Supervised Learning
Show steps
-
Practice CART tree analysis on real-world datasets.
-
Evaluate and compare multiple supervised learning algorithms.
-
Apply supervised learning to solve marketing problems.
Campaign Analysis Report
Show steps
Demonstrate campaign analysis skills and reinforce understanding of testing methods.
Browse courses on
Campaign Analysis
Show steps
-
Conduct a campaign analysis using cross-validation.
-
Write a report summarizing the findings and recommendations.
Practice Unsupervised Learning Techniques
Show steps
To deepen your understanding of unsupervised learning, engage in practice drills that reinforce techniques like PCA and clustering, enhancing your ability to uncover hidden patterns in marketing data.
Browse courses on
Unsupervised Learning
Show steps
-
Complete exercises on dimensionality reduction using PCA
-
Apply clustering algorithms to segment customer data
-
Interpret the results of unsupervised learning models
Unsupervised Learning Tutorials
Show steps
Deepen understanding of unsupervised learning algorithms for advanced segmentation and market analysis.
Browse courses on
Unsupervised Learning
Show steps
-
Follow online tutorials on unsupervised learning techniques.
-
Implement unsupervised learning algorithms on marketing data.
-
Experiment with different algorithms to optimize results.
Explore Unsupervised Learning Techniques
Show steps
Gain practical insights into unsupervised learning algorithms and their applications
Show steps
-
Follow online tutorials or video courses on unsupervised learning
-
Experiment with different clustering and dimensionality reduction techniques
-
Apply unsupervised learning to real-world data sets
Participate in Marketing Analytics Challenges
Show steps
To challenge your skills and apply your learning, participate in marketing analytics challenges or competitions to solve real-world problems and showcase your abilities.
Show steps
-
Identify relevant marketing analytics challenges or competitions
-
Form a team or participate individually
-
Develop and implement data-driven solutions
Marketing ML Tools and Resources
Show steps
Curate a valuable collection of resources to support ML implementation in marketing.
Browse courses on
Machine Learning Tools
Show steps
-
Research and identify relevant marketing ML tools.
-
Organize and document the collection.
-
Share the compilation with the learning community.
Attend Industry-Specific Workshop
Show steps
Enhance your practical knowledge by attending workshops focused on machine learning in marketing
Browse courses on
AI in Marketing
Show steps
-
Identify relevant workshops or conferences
-
Register and attend the workshop
-
Actively participate in discussions and hands-on sessions
Develop a Marketing Case Study
Show steps
Apply your learning by creating a case study that demonstrates the use of machine learning in marketing
Show steps
-
Identify a real-world marketing scenario
-
Collect and analyze relevant data
-
Develop and implement a machine learning solution
-
Evaluate the results and write a comprehensive report
Enhance Your Statistical Analysis Skills
Show steps
Refresh your statistical analysis skills to strengthen your foundation for the advanced techniques covered in this course.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Review materials on basic statistical concepts
- Practice solving statistical problems
- Ensure proficiency in using statistical software
Review Essential Statistics
Show steps
Bring statistics knowledge from a previous course up to speed for more successful learning in this program.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes from a previous course on statistics.
- Complete online exercises or practice problems.
- Take a practice quiz.
Review ML Basics
Show steps
Review essential machine learning fundamentals and brush up on supervised learning concepts
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Recall key concepts of supervised learning
- Go through notes and materials from previous ML courses or tutorials
- Complete practice questions or exercises
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Participate in Discussion Groups
Show steps
Engage in discussions with peers to clarify concepts, exchange ideas, and gain diverse perspectives on the topics covered in this course.
Show steps
Show steps
Show steps
- Join a peer discussion group or forum
- Actively participate in discussions related to course topics
- Ask questions and provide insights to enhance understanding
Learn about Decision Trees
Show steps
To enhance your understanding of decision trees, explore guided tutorials focusing on their structure, algorithms, and applications in marketing.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Follow tutorials on the concepts of decision trees
- Explore examples of CART tree analysis
- Practice implementing decision trees for marketing use cases
Join a Study Group
Show steps
Enhance your understanding through discussions and collaboration with peers
Browse courses on
Collaborative Learning
Show steps
Show steps
Show steps
- Reach out to classmates or fellow learners
- Organize regular study sessions
- Discuss course materials, share insights, and work on assignments together
Attend Predictive Analytics Workshops
Show steps
To enhance your knowledge and practical skills, attend workshops focused on predictive analytics, gaining hands-on experience with techniques and tools used in marketing.
Show steps
Show steps
Show steps
- Research and identify relevant predictive analytics workshops
- Register and participate in the workshops
- Engage with industry experts and practitioners
Study Group Discussions
Show steps
Enhance understanding through collaboration, knowledge sharing, and peer feedback.
Show steps
Show steps
Show steps
- Join or create a study group with fellow learners.
- Discuss course concepts and share insights.
- Provide feedback and support to group members.
Practice Predictive Modeling
Show steps
Sharpen your predictive modeling skills through repetitive exercises
Browse courses on
Predictive Analytics
Show steps
Show steps
Show steps
- Use online platforms or textbooks for practice problems
- Participate in coding challenges or competitions
- Analyze real-world datasets and make predictions
Supervised Learning Drills
Show steps
Gain proficiency in supervised learning techniques to enhance predictive accuracy.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Practice CART tree analysis on real-world datasets.
- Evaluate and compare multiple supervised learning algorithms.
- Apply supervised learning to solve marketing problems.
Campaign Analysis Report
Show steps
Demonstrate campaign analysis skills and reinforce understanding of testing methods.
Browse courses on
Campaign Analysis
Show steps
Show steps
Show steps
- Conduct a campaign analysis using cross-validation.
- Write a report summarizing the findings and recommendations.
Practice Unsupervised Learning Techniques
Show steps
To deepen your understanding of unsupervised learning, engage in practice drills that reinforce techniques like PCA and clustering, enhancing your ability to uncover hidden patterns in marketing data.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Complete exercises on dimensionality reduction using PCA
- Apply clustering algorithms to segment customer data
- Interpret the results of unsupervised learning models
Unsupervised Learning Tutorials
Show steps
Deepen understanding of unsupervised learning algorithms for advanced segmentation and market analysis.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Follow online tutorials on unsupervised learning techniques.
- Implement unsupervised learning algorithms on marketing data.
- Experiment with different algorithms to optimize results.
Explore Unsupervised Learning Techniques
Show steps
Gain practical insights into unsupervised learning algorithms and their applications
Show steps
Show steps
Show steps
- Follow online tutorials or video courses on unsupervised learning
- Experiment with different clustering and dimensionality reduction techniques
- Apply unsupervised learning to real-world data sets
Participate in Marketing Analytics Challenges
Show steps
To challenge your skills and apply your learning, participate in marketing analytics challenges or competitions to solve real-world problems and showcase your abilities.
Show steps
Show steps
Show steps
- Identify relevant marketing analytics challenges or competitions
- Form a team or participate individually
- Develop and implement data-driven solutions
Marketing ML Tools and Resources
Show steps
Curate a valuable collection of resources to support ML implementation in marketing.
Browse courses on
Machine Learning Tools
Show steps
Show steps
Show steps
- Research and identify relevant marketing ML tools.
- Organize and document the collection.
- Share the compilation with the learning community.
Attend Industry-Specific Workshop
Show steps
Enhance your practical knowledge by attending workshops focused on machine learning in marketing
Browse courses on
AI in Marketing
Show steps
Show steps
Show steps
- Identify relevant workshops or conferences
- Register and attend the workshop
- Actively participate in discussions and hands-on sessions
Develop a Marketing Case Study
Show steps
Apply your learning by creating a case study that demonstrates the use of machine learning in marketing
Show steps
Show steps
Show steps
- Identify a real-world marketing scenario
- Collect and analyze relevant data
- Develop and implement a machine learning solution
- Evaluate the results and write a comprehensive report
Career center
Learners who complete Machine Learning for Marketers will develop knowledge and skills
that may be useful to these careers:
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data analysis to better understand customer behavior and trends. This course can help build foundational machine learning skills that can be applied to marketing problems.
Digital Marketing Specialist
Digital Marketing Specialist
A Digital Marketing Specialist is responsible for creating and executing digital marketing campaigns. This course can help build a foundation in machine learning, a skillset that is increasingly being used by Digital Marketing Specialists to improve the effectiveness of their campaigns.
Marketing Manager
Marketing Manager
A Marketing Manager uses their knowledge of marketing principles to create and execute marketing campaigns. This course can help build a foundation in machine learning, a skillset that is increasingly being used by Marketing Managers to improve the effectiveness of their campaigns.
Product Analyst
Product Analyst
A Product Analyst analyzes data to help businesses understand how their products are performing and how they can be improved.
Business Analyst
Business Analyst
A Business Analyst uses data analysis to help businesses understand their operations and make better decisions.
Product Manager
Product Manager
A Product Manager is responsible for the development and marketing of a product. This course can help build machine learning skills that can be applied to product development and marketing.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data and make predictions. This course can help build machine learning skills that can be applied to quantitative analysis, such as data analysis and predictive modeling.
Market Researcher
Market Researcher
A Market Researcher conducts market research to better understand customer needs and preferences. This course can help build machine learning skills that can be applied to market research, such as data analysis and predictive modeling.
Decision Scientist
Decision Scientist
A Decision Scientist uses data analysis to help businesses make better decisions.
Customer Success Manager
Customer Success Manager
A Customer Success Manager builds and maintains relationships with customers to ensure that they are satisfied with a company's products or services.
Consultant
Consultant
A Consultant provides advice and guidance to clients on a variety of business issues. This course can help build machine learning skills that can be applied to consulting, such as data analysis and problem-solving.
Data Engineer
Data Engineer
A Data Engineer designs and builds data systems to store and process large amounts of data. This course can help build machine learning skills that can be applied to data engineering, such as data analysis and data management.
Customer Relationship Manager
Customer Relationship Manager
A Customer Relationship Manager (CRM) is responsible for managing relationships with customers to build loyalty.
Data Scientist
Data Scientist
A Data Scientist combines machine learning, computer programming, and statistical skills to analyze, model, and interpret large amounts of data. This course helps build a foundation in machine learning, a skillset that is highly sought in the field of Data Science.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course can help build machine learning skills that can be applied to software engineering, such as data analysis and algorithm development.
For more career information including salaries, visit:
OpenCourser.com/course/jjrg8l/machine
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning for Marketers.
Provides a comprehensive overview of statistical learning methods, making it a great resource for those who want to understand the theoretical foundations of machine learning.
Provides a practical guide to implementing machine learning algorithms using popular Python libraries, making it a great resource for those who want to apply machine learning to real-world problems.
Provides a practical guide to implementing machine learning algorithms in R, making it a great resource for those who want to apply machine learning to real-world problems.
Provides a practical guide to implementing machine learning algorithms in Python, making it a great resource for those who want to apply machine learning to real-world problems.
Save
Provides a practical guide to using Microsoft Excel to perform marketing analytics, making it a great resource for those who want to use data to improve their marketing.
Provides a comprehensive overview of digital marketing analytics, making it a great resource for those who want to learn how to use data to improve their digital marketing campaigns.
Provides a comprehensive overview of predictive analytics techniques, making it a great resource for those who want to learn how to use data to predict future outcomes.
Save
Provides a comprehensive overview of marketing management, making it a great resource for those who want to learn the fundamentals of marketing.
Provides a comprehensive overview of machine learning concepts and techniques, making it a great starting point for those new to the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jjrg8l/machine
Share
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Effort
4 weeks of study
Level
Intermediate
Via
Coursera
Institution
University of Colorado System
Instructors
A.W. Lukens
Tony Cox, Jr.
Ric Mills
Language
English
This course belongs to a
collection
called
Data Science for Marketing. It's comprised of four courses:
- Machine Learning for Marketers (viewing)
- Time, Change, and Decisions for Marketing
- Regression Modeling for Marketers
- Customer Data Analytics for Marketers
Good to know
Investigates machine learning strategies for marketing, which is an active area of research and practice with high impact in the modern economy
Taught by instructors with relevant names in machine learning and marketing
Covers practical machine learning methods applied in marketing that help learners create predictive and decision-making models
Provides hands-on experience with real-world marketing scenarios and data using supervised and unsupervised learning
Requires background in machine learning and marketing concepts
Doesn't cover advanced topics such as natural language processing for marketing
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