In this episode, we'll dive deep into one of the foundations of modern data science, Bayesian

algorithms and Bayesian thinking. Join me along with guest Max Sclar as we look at the algorithmic

side of data science. This is Talk Python to Me, episode 23 podcast on Python, the language, the libraries, the ecosystem, and the personalities.

This is your host, Michael Kennedy. Follow me on

Twitter where I'm at M. Kennedy. Keep up with a show and listen to past episodes at talk python.

And follow the show on Twitter via at talk python. This episode is brought to you by

Linode and TidLift. Please check out what they're offering during their segments. It really helps

support the show. Max,

welcome to talk Python to me. Thanks for having me, Michael. It's very great to be on. It's great to have

you on as well. You've been on Python bytes before, but never talk Python to me. That was a lot

of fun. I actually got someone reached out to me on Twitter the other day saying, hey, I saw you on

Python bytes. So that was really exciting. Right on, right on.

That's super cool.

I heard you on Python bytes.

I always say saw you when it's really heard you.

But anyway.

It's all good.

So now they can say they saw you on Talk PythonMe as well.

Now, we're going to talk about some of the foundational ideas behind data science,

machine learning.

That's going to be a lot of fun.

Yeah. Before we get to them, let's set the stage and give people a sense of where you're coming

...