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 seven, logistic regression.

In this episode, we're going to talk about classifiers, namely the logistic regression

classifier algorithm.

So remember where we are in the artificial intelligence tree.

We've gone down to machine learning, down to supervised learning, and then supervised learning

is broken down into two subfields, classification and regression.

We've studied regression last episode with linear regression, which will give you a continuous

variable output, a number, so the cost of a house in Portland, Oregon. And now we're going to talk about

classification, which will give you the class of a thing. Am I looking at a cat, a dog, a tree,

a house? Or in the case of binary classification, is this a dog? Yes or no? Zero or one.

Now, you may have noticed right off the bat the word logistic regression. That is really

confusing. Wait, I said that supervised learning is broken down to into regression and classification.

So those are two separate categories. And now we're talking about logistic and linear regression.

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