Unsupervised Machine Learning. K-Means & Hierarchical Clustering

1. 1Apply K-Means clustering to a sample dataset using scikit-learn in Python to identify cluster assignments
2. 2Run Hierarchical Clustering on a dataset using SciPy to visualize dendrograms and understand cluster relationships
3. 3Configure clustering hyperparameters, such as the number of clusters (K) and distance metrics, to optimize model performance
4. 4Test the robustness of clustering models using metrics like silhouette scores and Calinski-Harabinski index
5. 5Compare the results of K-Means and Hierarchical Clustering on the same dataset to determine the most suitable approach

Data scientists and analysts can benefit from this knowledge to uncover hidden insights in their datasets, while machine learning engineers can apply these techniques to improve model performance

💡 Unsupervised machine learning can reveal valuable insights in unlabeled data, and K-Means and Hierarchical Clustering are essential techniques for this purpose

Uncover hidden patterns in your data with unsupervised machine learning using K-Means & Hierarchical Clustering!