MISSION 40

K-means Clustering

Learn how to use K-means clustering to group together similar NBA players.

Objectives

  • Learn to implement the k-means clustering algorithm from scratch.
  • Learn some of the challenges of k-means clustering.

Mission Outline

1. Clustering NBA Players
2. Point Guards
3. Points Per Game
4. Assist Turnover Ratio
5. Visualizing the Point Guards
6. Clustering players
7. The Algorithm
8. Visualize Centroids
9. Setup (continued)
10. Step 1 (Euclidean Distance)
11. Step 1 (Continued)
12. Visualizing Clusters
13. Step 2
14. Repeat Step 1
15. Repeat Step 2 and Step 1
16. Challenges of K-Means
17. Conclusion
18. Takeaways

machine-learning-intermediate

Course Info:

Intermediate

The median completion time for this course is 5.6 hours. View Details

This course requires a premium subscription and includes 1 free mission and 5 paid missions, which includes 1 guided project.  It is the 21st course in the Data Scientist in Python path.

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