Data Science: Natural Language Processing (NLP) in Python

Data Science Natural Language Processing Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.

What you will learn

  • Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models
  • Write your own spam detection code in Python
  • Write your own sentiment analysis code in Python
  • Perform latent semantic analysis or latent semantic indexing in Python
  • Have an idea of how to write your own article spinner in Python

Requirements of Data Science Natural Language Processing:

  • Install Python, it’s free!
  • You should be at least somewhat comfortable writing Python code
  • Know how to install numerical libraries for 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 of Data Science Natural Language Processing:

In Data Science Natural Language Processing course you will construct MULTIPLE useful frameworks utilizing regular language handling, or NLP – the part of AI and information science that manages text and discourse. This course isn’t important for my profound learning arrangement, so it doesn’t contain any hard math – only directly up coding in Python. Every one of the materials for this course are FREE.

After a concise conversation about what NLP is and what it can do, we will start assembling helpful stuff. The primary thing we’ll fabricate is a code decoding calculation. These have applications in fighting and surveillance. We will figure out how to construct and apply a few valuable NLP apparatuses in this segment, to be specific, character-level language models (utilizing the Markov guideline), and hereditary calculations.

The subsequent task, where we start to utilize more conventional “AI”, is to construct a spam locator. You probably get next to no spam nowadays, contrasted with say, the mid 2000s, due to frameworks like these.

Next we’ll fabricate a model for notion examination in Python. This is something that permits us to dole out a score to a square of text that discloses to us how certain or negative it is. Individuals have utilized opinion investigation on Twitter to anticipate the securities exchange.

We’ll go over some useful apparatuses and strategies like the NLTK (common language tool compartment) library and inactive semantic examination or LSA.

At long last, we end the course by building a text rewriter. This is a difficult issue and even the most famous items out there these days don’t take care of business. These talks are intended to simply kick you off and to give you thoughts for how you may develop them yourself. When dominated, you can utilize it as a SEO, or website streamlining instrument. Web advertisers wherever will adore you on the off chance that you can do this for them!

This course centers around “how to construct and comprehend”, not only “how to utilize”. Anybody can figure out how to utilize an API shortly subsequent to perusing some documentation. It’s not tied in with “recollecting realities”, it’s tied in with “seeing with your own eyes” through experimentation. It will show you how to envision what’s going on in the model inside. On the off chance that you need something beyond a shallow gander at AI models, this course is for you.

“On the off chance that you can’t actualize it, you don’t get it”

Or on the other hand as the incredible physicist Richard Feynman said: “What I can’t make, I don’t comprehend”.

My courses are the ONLY courses where you will figure out how to actualize AI calculations without any preparation

Different courses will show you how to connect your information into a library, however do you truly require assist with 3 lines of code?

In the wake of doing likewise with 10 datasets, you understand you didn’t learn 10 things. You learned 1 thing, and just rehashed similar 3 lines of code multiple times…

Recommended Prerequisites:

Python coding: if/else, circles, records, dicts, sets

Take my free Numpy essentials course (it’s FREE, no reasons!) to find out about Numpy, Matplotlib, Pandas, and Scikit-Learn, just as Machine Learning fundamentals

Discretionary: If you need to comprehend the numerical parts, straight variable based math and likelihood are useful

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Look at the talk “AI and AI Prerequisite Roadmap” (accessible in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

  • Students who are comfortable writing Python code, using loops, lists, dictionaries, etc.
  • Students who want to learn more about machine learning but don’t want to do a lot of math
  • Professionals who are interested in applying machine learning and NLP to practical problems like spam detection, Internet marketing, and sentiment analysis
  • This course is NOT for those who find the tasks and methods listed in the curriculum too basic.
  • This course is NOT for 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 for 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 for, you are too far behind to take this course.

Course content of Data Science Natural Language Processing:

Introduction of Data Science Natural Language Processing:

  • Introduction and Outline
  • Why Learn NLP?
  • The Central Message of this Course (Big Picture Perspective)

Course Preparation:

  • Anyone Can Succeed in this Course
  • Where to get the code and data
  • How to Open Files for Windows Users

Machine Learning Basics Review:

  • Machine Learning: Section Introduction
  • What is Classification?
  • Classification in Code
  • What is Regression?
  • Regression in Code
  • What is a Feature Vector?
  • Machine Learning is Nothing but Geometry
  • All Data is the Same
  • Comparing Different Machine Learning Models
  • Machine Learning and Deep Learning: Future Topics
  • Section Summary

Decrypting Ciphers:

  • Section Introduction
  • Ciphers
  • Language Models
  • Genetic Algorithms
  • Code Preparation
  • Link to Cipher Notebook
  • Code pt 1
  • Code pt 2
  • Code pt 3
  • Code pt 4
  • Code pt 5
  • Code pt 6
  • Section Conclusion

Build your own spam detector:

  • Build your own spam detector – description of data
  • Build your own spam detector using Naive Bayes and AdaBoost – the code
  • Key Takeaway from Spam Detection Exercise
  • Naive Bayes Concepts
  • AdaBoost Concepts
  • Other types of features
  • Spam Detection FAQ (Remedial #1)
  • What is a Vector? (Remedial #2)
  • SMS Spam Example
  • SMS Spam in Code
  • Suggestion Box

Build your own sentiment analyzer:

  • Description of Sentiment Analyzer
  • Logistic Regression Review
  • Preprocessing: Tokenization
  • Preprocessing: Tokens to Vectors
  • Sentiment Analysis in Python using Logistic Regression
  • Sentiment Analysis Extension
  • How to Improve Sentiment Analysis & FAQ

NLTK Exploration:

  • NLTK Exploration: POS Tagging
  • NLTK Exploration: Stemming and Lemmatization
  • NLTK Exploration: Named Entity Recognition
  • Want more NLTK?

Latent Semantic Analysis:

  • Latent Semantic Analysis – What does it do?
  • SVD – The underlying math behind LSA
  • Latent Semantic Analysis in Python
  • What is Latent Semantic Analysis Used For?
  • Extending LSA

Write your own article spinner:

  • Article Spinning Introduction and Markov Models
  • Trigram Model
  • More about Language Models
  • Precode Exercises
  • Writing an article spinner in Python
  • Article Spinner Extension Exercises

How to learn more about NLP:

  • What we didn’t talk about

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