In this video, we cover K-Means clustering in detail. The session gives both theoretical and practical knowledge of this clustering algorithm. The session begins by highlighting the difference between Supervised and Unsupervised learning. Two important use cases of clustering algorithms are discussed. The necessary steps for developing K-Means clustering are discussed including the math involved. Finally, the session ends with a detailed demonstration of K-Means clustering using Google Colab and ML libraries such as Sklearn.
If you are new to Python programming and ML, kindly watch my previous videos on ML (Python programming Series playlist). Link is below:
https://youtu.be/L-s2JgxvAEM?si=W3VHFKHUj-0VeA2_
