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

  • 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


  • 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


In this Data Science: Natural Language Processing (NLP) in Python course, you will develop MULTIPLE useful systems utilizing natural language processing, or NLP – the branch of machine learning and data science that handles text and speech. This course is not part of my deep knowing series, so it does not consist of any difficult math – simply directly coding in Python. All the products for this course are FREE.

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After a short discussion about what NLP is and what it can do, we will start developing really helpful things. The first thing we’ll develop is a cipher decryption algorithm. These have applications in warfare and espionage. We will discover how to develop and use a number of beneficial NLP tools in this area, particularly, character-level language models (utilizing the Markov concept), and genetic algorithms.

The 2nd job, where we start to utilize more conventional “machine learning”, is to develop a spam detector. You likely get extremely little spam nowadays, compared to say, the early 2000s, because of systems like these.

Next we’ll develop a model for belief analysis in Python. This is something that permits us to appoint a rating to a block of text that informs us how favorable or unfavorable it is. Individuals have actually utilized belief analysis on Twitter to forecast the stock market.

We’ll review some useful tools and strategies like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.

We end the Data Science: Natural Language Processing (NLP) in Python course by developing a post spinner. This is a really difficult issue and even the most popular items out there these days do not get it. These lectures are created to simply get you began and to provide you concepts for how you may improve on them yourself. As soon as mastered, you can utilize it as an SEO, or seo tool. Web online marketers all over will enjoy you if you can do this for them!

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This course concentrates on “how to develop and comprehend”, not simply “how to utilize”. Anybody can find out to utilize an API in 15 minutes after checking out some paperwork. It’s not about “keeping in mind realities”, it’s about “seeing on your own” through experimentation. It will teach you how to visualize what’s taking place in the model internally. If you desire more than simply a superficial take a look at machine learning models, this course is for you.

Recommended Prerequisites:

  • Python coding: if/else, loops, lists, dicts, sets
  • Take my complimentary Numpy requirements course (it’s FREE, no excuses!) to find out about Numpy, Matplotlib, Pandas, and Scikit-Learn, in addition to Machine Learning essentials

Optional: If you wish to comprehend the math parts, direct algebra and likelihood are handy

TIPS (for surviving the course):.

  • Watch it at 2x.
  • Take handwritten notes. This will significantly increase your capability to maintain the info.
  • Ask great deals of concerns on the discussion board. The more the much better!
  • Recognize that many workouts will take you days or weeks to finish.
  • Compose code yourself, do not simply sit there and take a look at my code.


  • Take a look at the lecture “What order should I take your courses in?” (readily available in the Appendix of any of my courses, consisting of the complimentary Numpy course).
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Who this course is for:.

  • Trainees who are comfortable composing Python code, utilizing loops, lists, dictionaries, and so on
  • Trainees who wish to discover more about machine learning however do not wish to do a great deal of math.
  • Experts who have an interest in using machine learning and NLP to useful issues like spam detection, Internet marketing, and belief analysis.
  • This Data Science: Natural Language Processing (NLP) in Python course is NOT for those who discover the tasks and approaches noted in the curriculum too fundamental.
  • This course is NOT for those who do not currently have a fundamental understanding of machine learning and Python coding (however you can discover these from my FREE Numpy course).
  • This course is NOT for those who do not understand (provided the area titles) what the function of each task is. E.g. if you do not understand what “spam detection” might be helpful for, you are too far behind to take this course.

Created by Lazy Programmer Inc.
Last updated 6/2020
English [Auto-generated], German [Auto-generated]

Size: 1.5GB

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