Welcome back to Machine Learning Guide. I'm your host, Tyler Rinelli.

MLG teaches the fundamentals of machine learning and artificial intelligence.

It covers intuition, models, math, languages, frameworks, and more.

Where your other machine learning resources provide the trees, I provide the forest.

Visual is the best primary learning modality, but audio is a great supplement during exercise commute and chores.

Consider MLG your syllabus with highly curated resources for each episode's details at OCdevel.com forward slash MLG.

I'm also starting a new podcast which could use your support. It's called

Lefnear's Life Hacks and teaches productivity focused tips and tricks, some which could prove

beneficial in your machine learning education journey. Find that at Ocdevel.com forward slash

LLH. You're listening to Machine Learning Applied.

In this episode, we're going to talk about practical clustering tools, per the usual

difference between Machine Learning Guide and Machine Learning Applied.

I won't be talking about any theory about clustering or how these clustering algorithms

work.

I'm just going to be talking about some of the

psychit learn packages and some of the tips and tricks that I've found useful in actually

applying clustering techniques in Nothi. And hopefully in a future machine learning guide episode,

I'll talk about the theory behind some of these tools and clustering in general, et cetera.

Now, the first tool I'm going to talk about is Scikit learn K means. Everyone knows K means.

K means is just the most popular clustering algorithm ever, just ever. It's everyone uses it 99% of the time if they're clustering,

they're using k means. And in fact, I just recommend trying k means first. If you need to cluster

your vectors, try k means. If it works great. If not, then you'll move on to basically

...