| Sumario: | A complete Python guide to Natural Language Processing to build spam filters, topic classifiers, and sentiment analyzers About This Video Build actual solutions backed by machine learning and Natural Language Processing models, instead of meandering in theory and mathematical symbols. Single-handedly build three models, one for spam filtering, 0ne for sentiment analysis, and finally one for text classification. Get the right foundation from which to do applied, actual Natural Language Processing. We show you how to get open sourced data, wrangle text into Python data structures with NLTK, and predict different classes of natural language with scikit-learn. In Detail There is an overflow of text data online nowadays. As a Python developer, you need to create a new solution using Natural Language Processing for your next project. Your colleagues depend on you to monetize gigabytes of unstructured text data. What do you do? Hands-on NLP with NLTK and scikit-learn is the answer. This course puts you right on the spot, starting off with building a spam classifier in our first video. At the end of the course, you are going to walk away with three NLP applications: a spam filter, a topic classifier, and a sentiment analyzer. There is no need for fancy mathematical theory, just plain English explanations of core NLP concepts and how to apply those using Python libraries. Taking this course will help you to precisely create new applications with Python and NLP. You will be able to build actual solutions backed by machine learning and NLP processing models with ease. This course uses Python 3.6, TensorFlow 1.4, NLTK 2, and scikit-learn 0.19, while not the latest version available, it provides relevant and informative content for legacy users of NLP with NLTK and Scikit-learn.
|
|---|
