These lessons will take you through various use cases and real-world applications of the methods we’ve covered in the course.
These lessons will take you through various use cases and real-world applications of the methods we’ve covered in the course. Use cases will cover various scenarios, including medicine, retail, and marketing scenarios.
These lessons will cover different methods of segmentation, including an examination of customer segmentation, image segmentation, and image categorization. Finally, we’ll look at the roles of privacy preservation, product bundles, and basket analysis.
Learning Objectives
- Learn real-world applications of clustering and association modeling
- Assess the challenges faced within real-world applications of clustering
- Examine key use cases to see machine learning in real-world applications
Skills you’ll gain
Azure Machine LearningCluster AnalysisMachine LearningMachine Learning AlgorithmsMachine Learning MethodsUse Case AnalysisWhat You'll Learn
- Explore real-world applications of clustering and association modeling across medicine, retail, and marketing scenarios
- Examine customer segmentation, image segmentation, and image categorization methods
- Assess the challenges faced within real-world applications of clustering
- Analyze the roles of privacy preservation, product bundles, and basket analysis
- Review key use cases that show machine learning applied in real-world settings
Key Takeaways
- Clustering and association modeling have real-world applications spanning medicine, retail, and marketing scenarios.
- Segmentation methods covered include customer segmentation, image segmentation, and image categorization.
- Real-world applications of clustering present challenges that the course examines.
- Product bundles and basket analysis are use cases explored alongside privacy preservation.
- The lessons walk through key use cases to demonstrate machine learning in real-world applications.
Frequently Asked Questions
What does this course cover?
It covers various use cases and real-world applications of clustering and association modeling, including customer segmentation, image segmentation, image categorization, product bundles and basket analysis, and privacy preservation across scenarios such as medicine, retail, and marketing.
What skills will I gain from this course?
The course builds skills in Azure Machine Learning, Cluster Analysis, Machine Learning, Machine Learning Algorithms, Machine Learning Methods, and Use Case Analysis.
What real-world scenarios are used as examples?
The use cases cover various scenarios, including medicine, retail, and marketing.
What lessons are included in this course?
The lessons are Customer Segmentation, Image Segmentation, Image Categorization, Product Bundles and Basket Analysis, and Privacy Preservation.
What are the learning objectives of this course?
Learners will learn real-world applications of clustering and association modeling, assess the challenges faced within those real-world applications, and examine key use cases to see machine learning in real-world applications.
Transcript
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Probably the most common use of clustering is in customer segmentation. The reason is by treating all of your customers as one large monolithic group, doesn't do very well when you're trying to figure out targeted offerings for customers that have different needs. It also allow you to have a more personalized approach when you are doing your marketing or when you're designing your products. Having just one single product for everyone doesn't do as well as having a series of products that meet customers needs in a more targeted way. Now, there are a number of ways to do customer segmentation, and the main difference between them is the kinds of variables that you are using for an input. Often, psychographics are used in order to determine which segments your customers belong to. And the advantage of using psychographic information is that it allows you to understand the motivating factors, and the worldview of your customers much better than looking at something that's relatively benign like demographic information or transactional history. So most marketers like to understand the psychographics of their various customer groups, even if they also look at the demographics, and other metrics around that. It's certainly possible to create a customer segmentation using only demographic variables. And in fact, there are a number of successful segmentation that have done exactly. These ones look at the overall lifestyle, and general purchase behavior, and then using that, they infer the interests of the segments. And so using this can be quite useful depending on what kind of product you have. If you're trying to target quite broad demographic groups, then this can be quite good in allowing you to understand which general groups may be most interested in your product. The other style of customer segmentation is a behavioral segmentation. So instead of looking at attitudes or psychographics, instead it looks at the actual behavior of the group. So this kind of segmentation is quite useful to understand the lifestyles, and actions of the groups based on their behaviors, and give you some insight into how you can effectively reach them. So now you should have an idea of the kinds of segmentations that you can do with customers and how clustering can be used in order to find those specific segments.
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