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 20, natural language processing part three.

This is the final episode on shallow natural language algorithms before we finally go into

deep learning natural language processing in the next episode. Remember that we're doing

the shallow, classical, traditional algorithms to NLP for foundational purposes.

So remember that we've been working with sort of three layers of NLP technology.

The first being real simple sort of cleanup stuff, regular expression-y text pre-processing and cleanup,

like lemitization, tokenization, stemming, stop wordprocessing and cleanup like lemitization tokenization stemming stopword removal and the

like then there's the middle layer of sort of power tools that we're going to be using along the way

syntax tools and nLP like part of speech tagging and named entity recognition and then there's

the high level of goals that we're trying to accomplish in NLP,

things like question answering, sentiment analysis, text classification, search engines, and the like.

We've already solved three high level goals of NLP.

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