Machine Learning Tutorial Python - 13: K Means Clustering Algorithm

K Means clustering algorithm is unsupervised machine learning technique used to cluster data points. In this tutorial we will go over some theory behind how k means works and then solve income group clustering problem using sklearn, kmeans and python. Elbow method is a technique used to determine optimal number of k, we will review that method as well.

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Topics that are covered in this Video:
0:00 introduction
0:08 Theory - Explanation of Supervised vs Unsupervised learning and how kmeans clustering works. kmeans is unsupervised learning
5:00 Elbow method
7:33 Coding (start) (Cluster people income based on age)
14:56 Use MinMaxScaler from sklearn
24:07 Exercise (Cluster iris flowers using their petal width and length)

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[arrahman]

You make Machine Learning so easy to understand. I would say you are a SAVER for the people who are struggling to understand different ML algorithums. Thank you so much. please if possible put some content on NLP.

[shauryabhatnagar]

You are probably one of the best teachers I have come across. Thank you so much!

[hshrestha]

Summarizing the algorithm for K Means clustering based on this video:
1. Start with k centroids by putting them at random points here k =2
2. Compute distance of every point from centroid and cluster them accordingly
3. Adjust centroid so they become center of gravity of given cluster
4. Again recluster every point based on distance with adjusted centroid
5. Reiterate until data points stop changing cluster
6. Again adjust centroids

[qas]

It's a blessing to be able to finally say that I can learn ML, thanks to you :). I have used 'HUE' from seaborn instead of writing plt.scatter for every group of the cluster. sns.scatterplot(df['Age'], df['Income($)'], hue = df['Cluster'])

[wyphonema]

my grad school professor explains this very badly. You explain things very well with patience, you are the definition of a good teacher

[cindinishimoto]

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Thanks, @codebasics <3

[AltafAnsari-tfnl]

I have started loving machine learning due to the simplicity of explanations.

[anitoons]

The project has been done. It does not require any preprocessing such as scaling. 3 clusters are forming here. It is the optimum value. Thank you, sir😊.

[nilupulperera]

What a beautiful explanation. The beauty of Data Science is shown in this video in a remarkable way.
The exercise is really beautiful!
Thank you very much, Sir.

[amilcarc.dasilva]

Excellent tutorial. This is highly recommended to watch. Thanks a lot Sir, I find it helpful in my project work....I really appreciate. You have done great work to help others. Keep up doing this great work.

[beansgoya]

Fantastic explanation. I like the way you showed us what happens if you don’t scale your features. You also waited for the perfect opportunity to show why we need to use the elbow method.

[sidduhedaginal]

Sir, you made Machine Learners life easy....amazing explanation that ever seen before and by Elbow technique we got K=3 for iris dateset.

[namansinghal]

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[RusCMRS]

I looked 5 min at start, and your teaching style for ML is spot on, better than the IIT professors. I am enjoying ML algo now. Thanks.

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was trying out tons of videos trying to understand the basics of ML, you made it so simple and quick.
Loved it!!

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you can't find a video and not watch all playlists, im so grateful to you, thank you sir!

[andreabrunelli]

Dear CodeBasics, your tutorials are way better than all the classes of the Master in AI I have just completed. Thank you very much!

[Kingsohio]

This is a great quick refresher for those with the basic knowledge of ML clustering algorithms

[bhaskarg]

your explanation is clear and clarity in the content.. and knowledge sharing to needed Data Science community is Nobel... thankyou... 🙏

[commercial]

This was awesome! I can't believe I learned how to do K-means clustering in just a few hours. Your explanations are clear and concise. Thank you so much!