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.

It's been a while.

I'm very sorry, everyone.

I got very busy for a minute there, but I'm getting back on the horse.

The next three episodes in this series are going to be on visualization, and in particular

on exploratory data analysis, or EDA.

Before we get into the visualization stuff, this episode is going to cover

Jupiter, Jupyter, J-U-P-Y-T-E-R. And I'm sure most, if not all of you, are already pretty well-versed

in Jupyter notebooks. They're pretty difficult to avoid when immersing yourself in machine learning.

Lots of blog posts, GitHub repositories, tutorials, et cetera, are done by way of Jupiter notebooks.

But I haven't covered them officially, so let's get that out of the way here.

The project Jupiter was previously called IPython notebooks.

They renamed it to Jupiter, J-U-P-Y-T-E-R, because now they're supporting multiple languages,

...