# Section 12: K-Means Clustering

So far we have done supervised learning. The remaining sections will be on unsupervised learning. Below is a quick guide on how to pick the estimator:

![scikit-learn-algo.jfif](https://cdn.hashnode.com/res/hashnode/image/upload/v1633109742615/ceCYjOr4V.jpeg)
Source: [scikit-learn](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) 

Unsupervised Learning:
(1) Clustering: Using features, group together data rows into distinct clusters
(2) Dimensionality Reduction: Using features, discover how to combine and reduce into fewer components

Supervised learning's performance metrics (RMSE or Accuracy) will not apply for unsupervised learning.

**References:
**
- [An Introduction to Statistical Learning](https://www.statlearning.com/)  (Download free pdf)
- [Jose Portilla's 2021 Python for Machine Learning & Data Science Masterclass](https://www.udemy.com/course/python-for-machine-learning-data-science-masterclass/)

![18_ml_12_30Sep21.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1633309923626/910e2jhLL.png)