Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com A Markov Chain is a system that transitions between states using a random, memoryless process. Markov Chains are a great tool for simulating real-world phenomena and environments with computers. In this video, we’ll give a specific example of how to use Markov Chains in Natural Language Generation. Watch this video to learn: – What is a Markov Chain – How are Markov Chains being used – The reasons they’re useful for Natural Language Generation
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. – Natural Language Processing (Part 1): Introduction to NLP & Data Science – Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python – Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python – Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python – Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python – Natural Language Processing (Part 6): Text Generation with Markov Chains in Python All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
Markov chains are used for keyboard suggestions, search engines, and a boatload of other cool things. In this video, I discuss the basic ideas behind Markov chains and show how to use them to generate random text. My code to generate text: https://github.com/unixpickle/markovchain My code to generate line drawings: https://github.com/unixpickle/markovdraw