ML-For-Beginners/5-Clustering/2-K-Means/README.md

# K-Means Clustering

[![Andrew Ng explains Clustering](https://img.youtube.com/vi/hDmNF9JG3lo/0.jpg)](https://youtu.be/hDmNF9JG3lo "Andrew Ng explains Clustering")

> 🎥 Click the image above for a video: Andrew Ng explains Clustering

## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)

In this lesson, you will learn how to create clusters using Scikit-Learn and the Nigerian music dataset you imported earlier. We will cover

- Data variance

### Introduction

### Prerequisite
### Preparation

Preparatory steps to start this lesson



✅ Knowledge Check - use this moment to stretch students' knowledge with open questions


## 🚀Challenge

Spend some time with this notebook, tweaking parameters. Can you improve the accuracy of the model by cleaning  the data more (removing outliers, for example)? What else can you do to create better clusters?

## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)

## Review & Self Study

Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?

Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html
) from Stanford

**Assignment**: [Try different clustering methods](assignment.md)