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Ryan Ahmed
In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation. Market segmentation is crucial for marketers since it enables them to launch targeted ad marketing campaigns that are tailored to customer's specific needs.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/vh0qut/unsupervised
What's inside
Syllabus
Unsupervised Machine Learning for Customer Segmentation
In this hands-on project, we will train an unsupervised machine learning algorithm to perform bank customer segmentation. This project could be practically applied at any marketing department in the banking and retail industries to segment customers into 'clusters' or 'groups'. In this hands-on project we will go through the following tasks: (1) Understand the problem statement and business case, (2) Import libraries and datasets, (3) Visualize and explore datasets, (4) Understand the theory and intuition behind k-means clustering machine learning algorithm, (5) Learn how to obtain the optimal number of clusters using the elbow method, (6) Use Scikit-Learn library to find the optimal number of clusters using elbow method, (7) Apply k-means using Scikit-Learn to perform customer segmentation, (8) Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops practical skills used in the banking and retail industries, which are highly relevant to business professionals
Emphasizes customer segmentation techniques, which are vital for targeted marketing campaigns
Provides practical hands-on projects, allowing learners to apply their knowledge directly to real-world scenarios
Utilizes industry-standard machine learning libraries such as Scikit-Learn, ensuring the relevance of the skills learned
Suitable for beginners in machine learning and marketing, providing a solid foundation in customer segmentation techniques
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Reviews summary
Excellent guided project: unsupervised machine learning
Learners say this guided project is excellent for developing a foundational skill in unsupervised machine learning. The instructor is knowledgeable and engaging, explaining concepts clearly and providing hands-on practice with projects. The course focuses on customer market segmentation using K-Means and PCA, and learners praise the detailed explanations and practical nature of the course.
Focuses on customer market segmentation using K-Means and PCA.
"A really good unsupervised learning case study for beginners to start with. Great explainations aside with its code."
"best practical course to understand unsupervised learning.faculty was precise and clear hoping to complete other courses from the faculty"
"Awesome course with interesting project. I learned a lot and something new."
Instructor is knowledgeable and engaging.
"The Prof. Ryan Ahmed is soooo Genius, thank you Prof."
"Great instructor. Should know Python prior to taking the course to get the most out of the project."
"best practical course to understand unsupervised learning.faculty was precise and clear hoping to complete other courses from the faculty"
Concepts explained clearly and thoroughly.
"Very well explained."
"Very good explanation and structure."
"Wonderful course to understand clustering basics. Ryan teaches the concepts as well as gives hands on practice in a very simplified way."
Learners highly recommend this course.
"Highly recommend it if anyone wants to learn how to do market segmentation using K-Means and PCA."
"One of the BEST Guided projects I've done so far. The instructor did his best to elaborate every code & entities in details."
"Awesome course with interesting project. I learned a lot and something new."
Course includes hands-on practice with projects.
"This is an excellent project-based course, with the right mix of theory and code."
"One of the best guided projects I have done so far. The instructor does their best in teaching what each part of the code entails."
"Awesome course with interesting project. I learned a lot and something new."
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 Unsupervised Machine Learning for Customer Market Segmentation with these
activities:
Compile resources on customer segmentation
Show steps
Expand your knowledge of customer segmentation by compiling a collection of useful resources such as articles, videos, and industry reports.
Browse courses on
Customer Segmentation
Show steps
-
Search for and identify reputable sources on customer segmentation
-
Organize and store the resources in a central location
Organize course materials
Show steps
Ensure you have a well-organized and accessible collection of all course materials, including notes, assignments, and resources.
Show steps
-
Create a dedicated folder or notebook for course materials
-
File and label materials systematically
-
Review and update your organization system regularly
Review intro software engineering concepts
Show steps
Refresh your knowledge in the basic concepts of software engineering including programming languages, software development, and best practices.
Browse courses on
Software Engineering
Show steps
-
Review notes and assignments from previous coursework
-
Review core software engineering principles
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Form study groups with classmates
Show steps
Collaborate with classmates to discuss course concepts, solve problems, and support each other's learning, particularly in areas related to customer segmentation.
Show steps
-
Find classmates who share similar learning goals
-
Schedule regular study sessions
-
Work together on assignments, projects, and practice exercises
Practice dimensionality reduction using PCA
Show steps
Gain hands-on experience with dimensionality reduction techniques commonly used in market segmentation and data analysis.
Browse courses on
Dimensionality Reduction
Show steps
-
Explore different PCA implementations in Scikit-Learn.
-
Experiment with different dimensionality reduction parameters.
-
Visualize the results of dimensionality reduction using scatter plots and other visualization techniques.
Follow Python and R tutorials
Show steps
Strengthen your understanding of the programming languages used in the course by following guided tutorials on Python and R.
Browse courses on
Python
Show steps
-
Find tutorials on Python and R that align with course content
-
Follow tutorials and work through exercises
-
Apply what you learn in tutorials to course assignments
Create a solution for customer market segmentation
Show steps
Develop a solution using the techniques from this course to segment a customer base into distinct market segments.
Browse courses on
Customer Segmentation
Show steps
-
Gather data on customer demographics, behavior, and preferences.
-
Visualize the data to identify patterns and relationships.
-
Apply unsupervised machine learning algorithms, such as k-means clustering, to segment customers into distinct groups.
-
Validate the segmentation solution by evaluating its effectiveness in predicting customer behavior and response to marketing campaigns.
Complete practice exercises on customer segmentation
Show steps
Enhance your understanding of customer segmentation techniques by completing practice exercises and solving case studies.
Browse courses on
Customer Segmentation
Show steps
-
Find practice exercises and case studies related to customer segmentation
-
Work through the exercises and analyze the results
-
Compare your solutions with others to identify areas for improvement
Write a blog post on customer segmentation best practices
Show steps
Reinforce your understanding by sharing your knowledge and insights on customer segmentation best practices.
Browse courses on
Customer Segmentation
Show steps
-
Summarize the key concepts and techniques covered in this course.
-
Share examples and case studies of successful customer segmentation strategies.
-
Discuss common pitfalls and challenges associated with customer segmentation.
Explore advanced clustering algorithms
Show steps
Explore more advanced clustering algorithms to better understand their strengths and limitations for customer segmentation.
Browse courses on
Clustering Algorithms
Show steps
-
Review hierarchical clustering and its application in customer segmentation.
-
Learn about density-based clustering algorithms, such as DBSCAN.
-
Implement these algorithms using Python libraries and compare their performance on a real-world dataset.
Develop a data visualization dashboard
Show steps
Apply your skills in data visualization by creating an interactive dashboard that effectively presents customer segmentation insights.
Browse courses on
Data Visualization
Show steps
-
Gather and analyze data relevant to customer segmentation
-
Choose appropriate data visualization techniques
-
Design and develop the interactive dashboard
Compile resources on customer segmentation
Show steps
Expand your knowledge of customer segmentation by compiling a collection of useful resources such as articles, videos, and industry reports.
Browse courses on
Customer Segmentation
Show steps
Show steps
Show steps
- Search for and identify reputable sources on customer segmentation
- Organize and store the resources in a central location
Organize course materials
Show steps
Ensure you have a well-organized and accessible collection of all course materials, including notes, assignments, and resources.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials
- File and label materials systematically
- Review and update your organization system regularly
Review intro software engineering concepts
Show steps
Refresh your knowledge in the basic concepts of software engineering including programming languages, software development, and best practices.
Browse courses on
Software Engineering
Show steps
Show steps
Show steps
- Review notes and assignments from previous coursework
- Review core software engineering principles
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Form study groups with classmates
Show steps
Collaborate with classmates to discuss course concepts, solve problems, and support each other's learning, particularly in areas related to customer segmentation.
Show steps
Show steps
Show steps
- Find classmates who share similar learning goals
- Schedule regular study sessions
- Work together on assignments, projects, and practice exercises
Practice dimensionality reduction using PCA
Show steps
Gain hands-on experience with dimensionality reduction techniques commonly used in market segmentation and data analysis.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Explore different PCA implementations in Scikit-Learn.
- Experiment with different dimensionality reduction parameters.
- Visualize the results of dimensionality reduction using scatter plots and other visualization techniques.
Follow Python and R tutorials
Show steps
Strengthen your understanding of the programming languages used in the course by following guided tutorials on Python and R.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find tutorials on Python and R that align with course content
- Follow tutorials and work through exercises
- Apply what you learn in tutorials to course assignments
Create a solution for customer market segmentation
Show steps
Develop a solution using the techniques from this course to segment a customer base into distinct market segments.
Browse courses on
Customer Segmentation
Show steps
Show steps
Show steps
- Gather data on customer demographics, behavior, and preferences.
- Visualize the data to identify patterns and relationships.
- Apply unsupervised machine learning algorithms, such as k-means clustering, to segment customers into distinct groups.
- Validate the segmentation solution by evaluating its effectiveness in predicting customer behavior and response to marketing campaigns.
Complete practice exercises on customer segmentation
Show steps
Enhance your understanding of customer segmentation techniques by completing practice exercises and solving case studies.
Browse courses on
Customer Segmentation
Show steps
Show steps
Show steps
- Find practice exercises and case studies related to customer segmentation
- Work through the exercises and analyze the results
- Compare your solutions with others to identify areas for improvement
Write a blog post on customer segmentation best practices
Show steps
Reinforce your understanding by sharing your knowledge and insights on customer segmentation best practices.
Browse courses on
Customer Segmentation
Show steps
Show steps
Show steps
- Summarize the key concepts and techniques covered in this course.
- Share examples and case studies of successful customer segmentation strategies.
- Discuss common pitfalls and challenges associated with customer segmentation.
Explore advanced clustering algorithms
Show steps
Explore more advanced clustering algorithms to better understand their strengths and limitations for customer segmentation.
Browse courses on
Clustering Algorithms
Show steps
Show steps
Show steps
- Review hierarchical clustering and its application in customer segmentation.
- Learn about density-based clustering algorithms, such as DBSCAN.
- Implement these algorithms using Python libraries and compare their performance on a real-world dataset.
Develop a data visualization dashboard
Show steps
Apply your skills in data visualization by creating an interactive dashboard that effectively presents customer segmentation insights.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and analyze data relevant to customer segmentation
- Choose appropriate data visualization techniques
- Design and develop the interactive dashboard
Career center
Learners who complete Unsupervised Machine Learning for Customer Market Segmentation will develop knowledge and skills
that may be useful to these careers:
Customer Segmentation Manager
Customer Segmentation Manager
Customer Segmentation Managers use data to segment customers and develop targeted marketing campaigns. This course provides a solid foundation in unsupervised machine learning, which is a key skill for Customer Segmentation Managers. This course would be helpful for individuals seeking to advance their career as Customer Segmentation Managers by building a solid foundation in unsupervised machine learning and gaining practical experience in applying it to customer segmentation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning models. This course provides a foundation in unsupervised machine learning, which is a key skill for Machine Learning Engineers. This course would be helpful for individuals seeking to advance their career as Machine Learning Engineers by building a solid foundation in unsupervised machine learning.
Data Analyst
Data Analyst
Data Analysts use various statistical and machine learning algorithms to find patterns and trends in data. This course provides a solid foundation in unsupervised machine learning, which is a key skill for Data Analysts. This course would be helpful for individuals seeking to advance their career as Data Analysts by building a solid foundation in unsupervised machine learning.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course provides a foundation in unsupervised machine learning, which is a key skill for Data Scientists. This course would be helpful for individuals seeking to advance their career as Data Scientists by building a solid foundation in unsupervised machine learning.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. This course provides a foundation in unsupervised machine learning, which can be used to segment customers and identify target markets. This course would be helpful for individuals seeking to advance their career as Marketing Managers by building a foundation in unsupervised machine learning and gaining practical experience in applying it to customer segmentation.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and make recommendations for improvement. This course provides a foundation in unsupervised machine learning, which can be used to segment customers and identify opportunities for growth. This course would be helpful for individuals seeking to advance their career as Business Analysts by building a foundation in unsupervised machine learning.
Statistician
Statistician
Statisticians use data to solve problems and make predictions. This course provides a foundation in unsupervised machine learning, which can be used to identify patterns and trends in data. This course would be helpful for individuals seeking to advance their career as Statisticians by building a foundation in unsupervised machine learning.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and manage risks. This course provides a foundation in unsupervised machine learning, which can be used to identify patterns and trends in data. This course would be helpful for individuals seeking to advance their career as Risk Analysts by building a foundation in unsupervised machine learning.
Product Manager
Product Manager
Product Managers develop and manage products and services. This course provides a foundation in unsupervised machine learning, which can be used to segment customers and identify product opportunities. This course would be helpful for individuals seeking to advance their career as Product Managers by building a foundation in unsupervised machine learning and gaining practical experience in applying it to customer segmentation.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course provides a foundation in unsupervised machine learning, which can be used to identify patterns and trends in data. This course would be helpful for individuals seeking to advance their career as Quantitative Analysts by building a foundation in unsupervised machine learning.
Market Research Analyst
Market Research Analyst
Market Research Analysts conduct research to understand market trends and customer needs. This course provides a foundation in unsupervised machine learning, which can be used to segment customers and identify target markets. This course would be helpful for individuals seeking to advance their career as Market Research Analysts by building a foundation in unsupervised machine learning.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course provides a foundation in unsupervised machine learning, which can be used to improve the performance and efficiency of databases. This course would be helpful for individuals seeking to advance their career as Database Administrators by building a foundation in unsupervised machine learning.
Software Engineer
Software Engineer
Software Engineers develop and maintain software applications. This course provides a foundation in unsupervised machine learning, which can be used to improve the performance and efficiency of software applications. This course would be helpful for individuals seeking to advance their career as Software Engineers by building a foundation in unsupervised machine learning.
Computer Scientist
Computer Scientist
Computer Scientists conduct research and develop new computer technologies. This course provides a foundation in unsupervised machine learning, which is a key area of research in computer science. This course would be helpful for individuals seeking to advance their career as Computer Scientists by building a foundation in unsupervised machine learning.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines to store and process data. This course provides a foundation in unsupervised machine learning, which can be used to improve the efficiency and performance of data pipelines. This course would be helpful for individuals seeking to advance their career as Data Engineers by building a foundation in unsupervised machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/vh0qut/unsupervised
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
Unsupervised Machine Learning for Customer Market Segmentation.
Provides a solid foundation in unsupervised machine learning algorithms, including k-means clustering and dimensionality reduction techniques such as Principal Component Analysis (PCA). It offers both mathematical explanations and practical examples, making it suitable for both beginners and experienced practitioners.
Provides a theoretical and conceptual framework for customer market segmentation, covering various segmentation approaches and their applications in marketing. It offers insights into the latest segmentation methodologies and their implications for marketing strategies.
Provides a practical introduction to machine learning with Python, covering both supervised and unsupervised learning methods. It includes a chapter dedicated to customer segmentation, providing a step-by-step guide to implementing algorithms and interpreting the results.
Offers a comprehensive introduction to machine learning, covering the fundamental concepts and techniques. It provides a chapter on unsupervised learning, including k-means clustering and PCA, making it a useful resource for understanding the basics of customer segmentation.
Provides a comprehensive guide to using SPSS Modeler for data mining and machine learning. It includes a chapter on customer segmentation, covering both k-means clustering and hierarchical clustering. The authors provide detailed instructions and real-world examples.
Provides a strategic perspective on customer relationship management, emphasizing the role of data in building and maintaining customer relationships. It includes a chapter on customer segmentation, covering both traditional and advanced approaches.
Focuses on practical applications of machine learning in marketing, including customer segmentation. It provides step-by-step instructions for implementing machine learning algorithms using real-world marketing data.
Offers a simplified introduction to customer analytics, including basic concepts of customer segmentation. It provides practical tips and examples for understanding customer behavior and using data to improve marketing efforts.
Offers a comprehensive overview of customer segmentation techniques, covering both traditional and advanced methods. It explores the theoretical foundations and practical implications of segmentation in marketing and provides insights into the latest trends and developments.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vh0qut/unsupervised
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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Develops practical skills used in the banking and retail industries, which are highly relevant to business professionals
Emphasizes customer segmentation techniques, which are vital for targeted marketing campaigns
Provides practical hands-on projects, allowing learners to apply their knowledge directly to real-world scenarios
Utilizes industry-standard machine learning libraries such as Scikit-Learn, ensuring the relevance of the skills learned
Suitable for beginners in machine learning and marketing, providing a solid foundation in customer segmentation techniques
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