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 19, natural language processing part two.

In this episode, we're going to jump into the classical natural language processing algorithms

that are used for the various tasks discussed in the previous episode.

Remember that natural language processing has something of a three-phase history.

The first part being hard-coded rules that come from the world of linguistics.

Remember another word for NLP is computational linguistics.

So NLP originated with a bunch of hard-coded rules, dictionaries, mappings, and the like.

And eventually, machine learning

was introduced to the field, which revolutionized so much of the work that was being done.

These were shallow learning algorithms. These are the classical, traditional approaches to natural

language processing. But the state of the art in NLP is deep learning. Just far and away,

it is widely agreed that the state of the art in NLP is

...