Bookmarks Data Science: Natural Language Processing (NLP) in Python | Udemy

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Data Science: Natural Language Processing (NLP) in Python | Udemy | 2.42 GB

What you’ll 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

In this course you will build MULTIPLE practical systems using natural language processing, or NLP – the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn’t contain any hard math – just straight up coding in Python. All the materials for this course are FREE.

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 algorithm. 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 algorithms.

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





“If you can’t implement it, you don’t understand it”





Suggested Prerequisites:


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



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.

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