Text classification is an interesting application of natural language processing. It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. These algorithms can perform sentiment analysis, create spam filters, and other applications. This tutorial will teach you how to train your binary text classifiers using Keras.

Comet ML is an experimentation platform, which permits testing Machine Learning projects, from the beginning up to the final monitoring. Many other similar platforms exist on the Web, including Neptune.ai, Comet ML can be easily integrated with the most popular Machine Learning libraries, including scikit-learn, Pytorch, Tensorflow, Keras, and so on. Experiments can be written in Python, Javascript, Java, R, and REST APIs. In this article, I focus on Python.