K-Means is one of the most popular "clustering" algorithms. It is used to solve the clustering problems in machine learning or data science. It uses the iterative process to find the optimal location of the centroids, which minimises the sum of distances between data points and the centroid. Through this video, our expert in AI, ML and Data Engineering – Raghavendra N engages the audience with the importance of K-means Cluster and how can one apply it on daily basis. He will discuss the steps involved in the K means clustering algorithm and how to implement it in Python using the scikit-learn library. By the end of this video, you will have a solid understanding of K means clustering and how to use it in your own projects.
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