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. Hello everyone. Welcome to another checkpoint episode.

These checkpoints are basically delineations between levels of machine learning learning.

You can think of them as the finish line to individual machine learning campaigns.

So everything prior to this checkpoint is shallow learning fundamentals,

and starting now everything after this checkpoint is deep learning fundamentals. You can use these

checkpoints as an opportunity to pause and go back and learn all of the details of shallow

learning, for example, by way of the resources section, or you can forge

on ahead into deep learning. Additionally, in these checkpoints, I'm going to boil down all of the

resources that I've mentioned in prior episodes into the most fundamental resources. So if you

want, you can ignore resources as I go through my episodes until these checkpoints

and use them as sort of a reader's digest if you're pressed for time or something.

So let's talk about the most essential resources that I've mentioned in all the prior episodes.

Resource number one, of course being the Andrew Ian Coursera course.

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