Data Science Natural Language Processing (NLP) in Python 2021

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Data Science: Natural Language Processing (NLP) in Python 2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 2.56 GB | Duration: 9h 55m
What you'll learn

Write your own cipher decryption algithm using genetic algithms and Language modeling with Markov models
Write your own spam detection code in Python
Write your own sentiment analysis code in Python
Perfm latent semantic analysis latent semantic indexing in Python
Have an idea of how to write your own article spinner in Python

Requirements
Install Python, it's free!
You should be at least somewhat comftable writing Python code
Know how to install numerical libraries f Python such as Numpy, Scipy, Scikit-learn, Matplotlib, and BeautifulSoup
Take my free Numpy prerequisites course (it's FREE, no excuses!) to learn about Numpy, Matplotlib, Pandas, and Scikit-Learn, as well as Machine Learning basics
Optional: If you want to understand the math parts, linear algebra and probability are helpful
Description
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After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a cipher decryption algithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level Language models (using the Markov principle), and genetic algithms.

The second project, where we begin to use me traditional "machine learning", is to build a spam detect. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.

Next we'll build a model f sentiment analysis in Python. This is something that allows us to assign a sce to a block of text that tells us how positive negative it is. People have used sentiment analysis on Twitter to predict the stock market.

We'll go over some practical tools and techniques like the NLTK (natural Language toolkit) library and latent semantic analysis LSA.

https://www.facebook.com/GFXHWS

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing f yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want me than just a superficial look at machine learning models, this course is f you.

"If you can't implement it, you don't understand it"

as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Python coding: if/else, loops, lists, dicts, sets

Take my free Numpy prerequisites course (it's FREE, no excuses!) to learn about Numpy, Matplotlib, Pandas, and Scikit-Learn, as well as Machine Learning basics

Optional: If you want to understand the math parts, linear algebra and probability are helpful

WHAT DER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is f:
Students who are comftable writing Python code, using loops, lists, dictionaries, etc.
Students who want to learn me about machine learning but don't want to do a lot of math
https://www.facebook.com/GFXHWS
This course is NOT f those who find the tasks and methods listed in the curriculum too basic.
This course is NOT f those who don't already have a basic understanding of machine learning and Python coding (but you can learn these from my FREE Numpy course).
This course is NOT f those who don't know (given the section titles) what the purpose of each task is. E.g. if you don't know what "spam detection" might be useful f, you are too far behind to take this course.

I recommends Buy premimum account for High speed+parallel downloads!

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