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.

This is the second of the visualization episodes.

In this one, we're going to talk about exploratory data analysis, aka EDA, as well as some charting

fundamentals.

So exploratory data analysis, I threw that phrase around a lot in the

last episode without describing it. EDA is part of a larger pipeline for your machine learning

process or your data science process. This whole umbrella of what's called business intelligence, B.I.

Or even just data science.

It's sort of the A to Z, the beginning to end pipeline of what you're working on.

The whole reason you have a machine learning model in the first place is part of a pipeline.

The first part of this pipeline is going to be getting your data from some data source,

maybe some data stream like Twitter or some sensors, and then you're going to maybe convert that

...