Learn Text Classification With Python and Keras (Overview)
Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this course, you’ll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text.
Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP).
00:00 Hello, and welcome! Thanks for joining me. You’re going to learn about practical text classification with Python and Keras. My name is Douglas Starnes, and I’ll be your guide for the next half hour.
00:13 This course is about natural language processing, or NLP. The field of natural language processing studies how computers can parse and extract information from natural language.
00:25 This is a big field, so in this course, you’ll focus on a single task often used in NLP: sentiment analysis.
00:33 The goal of sentiment analysis is to predict the mood of a body of text. For example, the text “That was the worst movie I’ve ever seen!” would have a pessimistic or negative sentiment. The phrase “I want to watch that movie again, it was fantastic!” would have an optimistic or positive sentiment.
00:53 The course will begin with some simple examples using scikit-learn and then it will get more advanced and close with building convolutional neural networks.
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