How KMeans Works and how to Train one in Python
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🌳 K-means clustering is a popular clustering algorithm used in data analysis and machine learning. Unlike hierarchical clustering, which organizes data points into a tree-like structure, k-means focuses on partitioning the data into a predetermined number of clusters. The algorithm works by iteratively assigning data points to clusters based on the mean (centroid) of the points in each cluster, followed by updating the centroids to minimize the overall variance within the clusters.
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📓 Notebook: https://colab.research.google.com/dri...
💾 Dataset: https://www.kaggle.com/datasets/camnu...
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00:00 - 11:40 - Theory Walkthrough
11:40 - 33:42 - Practical Implementation
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