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 about the lay of the land in Python natural

language processing tools, hugging face transformers, sentence transformers, Gensim,

Spacey, NLTK.

So here's a little bit of history on these packages as I

understand the history. So take it with a grain of salt. This has sort of just been my own

experience in evolving through using these packages over the years. It seems it all started with

NLTK, NLTK Natural Language Toolkit, seems to have been one of the most, if not the most

first popular NLP library.

NLTK basically lets you do anything and everything in NLP.

They just kept adding and adding and adding. All the features you could possibly imagine for any NLP application you could possibly imagine.

All the simple stuff like tokenization, stemming, and lemitization,

to more complex things like document classification and syntax tree parsing.

...