The article guides developers on how to perform binary text classification with neural networks using Twilio and TensorFlow in Python. It starts by setting up a virtual environment, downloading necessary libraries, and preparing training data in JSON format. The data is then processed through lemmatization, stemming, and bag-of-words model to convert it into numerical form that can be passed to TensorFlow. A deep neural network (DNN) is built using three fully-connected intermediate layers with softmax activation function. The DNN is trained on the prepared data using gradient descent algorithm, and its performance is tested by sending text messages through Twilio. The article concludes by encouraging readers to experiment with different NLP models, activation functions, and applications.