Python and Data Science - Data Visualization, Introduction t

Customer Segmentation using K-Means

Have you ever thought about understanding the basic concepts of Machine Learning by doing some simple hands-on activities. In this course, we will focus on K-Means algorithm with hands on project.

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Course curriculum

1. Prerequisites for this course
2. Notebook which we will be using in this course
3. Problem Statement
4. Understand our data
5. Visualize our data
6. Challenge problem
7. Use K-Means
8. Predict Cluster
1. Recap
2. Recap continued
3. K-Means with sklearn
4. Centroid locations
5. Inertia of the model
6. Determin Inertia
7. Need for preprocessing
8. K-Means expectation
9. Perform preprocessing
10. Column transformation
11. Create pipeline
12. Combine preprocessing with training
13. Evaluation
14. Suggested Reading
15. Choosing k
16. The Elbow method
17. Use the Elbow method
18. Retrain model
19. How to decide best model
1. Analyze Results
2. Increase k value
3. Challenge problem
4. Congratulations on completing this course!