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 14, shallow learning algorithms part three.

This is the third and final of the shallow learning algorithms episode, and this episode will

actually be quite easy by comparison to the last two.

The algorithms that we're going to cover in this episode are recommender systems, anomaly detection systems, and mark off chains.

Recommender systems and anomaly detection systems are extremely easy to understand.

So we'll start with those.

They're also very specifically applied.

So you're not going to use these algorithms maybe all over the place like we did with the last few algorithms, namely support vector machines, naive bays, and decision trees have a lot of

applications, and it's difficult to know when to use which where. These algorithms, recommender

systems, and anomaly detection are used specifically in those applications, recommending things and detecting anomalies.

Let's actually start with anomaly detection because that's even the easiest to understand.

So first off, what would you use an anomaly detection for?

For example, fraud detection in credit cards.

...