Explore clustering with scikit-learn, focusing on unsupervised learning techniques like K-Means, DBSCAN, cosine similarity, nearest neighbors, and cluster evaluation.

Become a Machine Learning Engineer

In this module, you'll explore clustering techniques using scikit-learn. We'll begin with cosine similarity and nearest neighbors, then delve into popular clustering algorithms like K-Means and DBSCAN. The module will also cover evaluating clusters and performing feature clustering, providing insights into unsupervised learning methods and their applications.

Unsupervised Learning

Here’s a quick summary of what we learned from the “Unsupervised Learning” module.


Final Remarks

Here’s a quick summary of what we learned from the “Unsupervised Learning” module.

Clustering with scikit-learn

Conclusion

Final Remarks