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. This is episode 15, performance. This episode is going to be the last

of the basics of machine learning, my friends. At this point, you have now gone through all the very

fundamental introductory components of machine learning, shallow learning, the basics of deep learning, a little bit of

the math, the technology. And finally, this episode where we're going to talk about performance

evaluation and performance improvement. You may have heard about things like high bias and

high variance, overfitting and underfitting, regularization, and then accuracy, confusion matrices, all these things.

This is going to be a very boring episode, unfortunately. I apologize. You're going to want

a coffee for this episode. Let's just get it over with. And at the end of this episode, you will

be an official machine learning Padawan, my friends.

You have come far.

Applaud yourself.

So let's do this episode so you can call yourself a machine learner.

In this episode, we're going to be talking about performance.

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