Data Science: Natural Language Processing (NLP) in Python
Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.
What you’ll learn
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Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models
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Write your own spam detection code in Python
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Write your own sentiment analysis code in Python
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Perform latent semantic analysis or latent semantic indexing in Python
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Have an idea of how to write your own article spinner in Python
Requirements
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Install Python, it’s free!
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You should be at least somewhat comfortable writing Python code
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Know how to install numerical libraries for Python such as Numpy, Scipy, Scikit-learn, Matplotlib, and BeautifulSoup
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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
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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.
