Explore unsupervised learning in this path focused on Clustering. Start with data preprocessing, learn algorithms like K-means, DBSCAN, and Hierarchical Clustering, and master validation techniques to evaluate model performance from scratch.
This learning path includes:
4 courses with bite-sized lessons and practices
17 engaging lessons in text and video formats
65 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Unlock the secrets of K-means clustering, the backbone of unsupervised learning. You will group data into clusters, identify cluster centroids, and refine cluster quality.
Unpack the complexity of hierarchical clustering, learning to construct and interpret dendrograms for valuable data insights, and apply your knowledge to real-world data.
Explore the nuanced world of density-based clustering. Learn to navigate through DBSCAN, focusing on connectivity and density functions to identify unique cluster shapes.
Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davis-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.
Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.