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. You're listening to Machine Learning Applied?

In this episode, we're going to talk a little bit more about NumPy and Pandas.

Some more depth that I didn't get into in the MLG languages and frameworks episode.

So NumPy and Pandas, in Python, when you're doing machine learning or exploratory data analysis or anything on data, you're going to be using NUMPI, NUMPI, which stands for numerical Python.

And NUMPI is a very old library. It's been around for a very long time. And what its goal is is to store big arrays of numbers in RAM and to operate over those arrays really fast.

Okay, so Numpai is just arrays of numbers or arrays of arrays of numbers.

Vectors or matrices or any dimensional tensors.

Remember, tensor, the word for n-dimensional array.

And in fact, in N-Py, we have a construct called N-D array, N-dimensional array,

which is synonymous with a tensor. N-D-array, tensor.

But in N-Pie, they call it an N-D array, and that's the main construct that you'll be using

when working with NUMPI. And what it does, what an N-D array does, is it stores a list of

numbers, or a list of a list of numbers numbers and so on. Those numbers can be integers

...