How to implement K-Means from scratch with Python

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In the 10th lesson of the Machine Learning from Scratch course, we will learn how to implement the K-Means algorithm.

Welcome to the Machine Learning from Scratch course by AssemblyAI.
Thanks to libraries like Scikit-learn we can use most ML algorithms with a couple of lines of code. But knowing how these algorithms work inside is very important. Implementing them hands-on is a great way to achieve this.

And mostly, they are easier than you’d think to implement.

In this course, we will learn how to implement these 10 algorithms.
We will quickly go through how the algorithms work and then implement them in Python using the help of NumPy.

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#MachineLearning #DeepLearning

AssemblyAI

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Hope you guys do gaussian mixture models (similar algorithm) next! That's part of my thesis work and i could use the help haha

stuartallen

I need fcm clustering and IFCM clustering can you help me?

muhammadadnanbashir

Good stuff, but pretty hard to comprehend fully at first

hounddog

What is the difference between using fit and predict?

_funkadelic

I enjoy all your videos, just a doubt why don't you use numpy broadcasting instead of using so many for loops to compute values such as the closest index and would reduce the runtime of your code.

ravindrakarande

Hi, great videos!! Just wondering, why defining euclidian distance outside of the class? This was also done in KNN video. All the best!

morisbagic

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