UDEMY 2021 - Data Science: Natural Language Processing (NLP) in Python

David K Mar 28, 2020

data_science_natural_language_processing_nlp_in_python.jpg | A&H Business Technology

Practical Applications of NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis.

You can find "Download Link" as a button at the end of this article.

What you’ll learn

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

Install Python, it’s free!

You should be at least somewhat comfortable writing Python code

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

Next we’ll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or 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 or LSA.

Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don’t get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them!

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 for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

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

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture “What order should I take your courses in?” (available in the Appendix 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.

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