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 13, shallow learning algorithms part two, support vector machines and naive

bays.

In this episode, I'm going to be talking about support vector machines and the naive bays

classifier.

These are two very powerful machine learning techniques, shallow learning algorithms. These are kind of those

power machine learning algorithms, power tools. I consider decision trees, support vector machines,

and naive bays. I consider them all three to be sort of power tools. A lot of the other shallow

learning algorithms that you'll learn are sort of dedicated to particular tasks or even if they're

multi-purpose, they shine under specific circumstances. But decision trees, support vector machines,

and naive bays are sort of these power tools that could be applied across a very wide spectrum

of machine learning applications. They're primarily built for classification. All three of these

algorithms are primarily built for classification,

...