Summary

In this chapter, you have learned the most important NLP techniques. There was a lot that we've learned, and here's a big list of things we covered in this chapter and everything you should now feel confident about understanding:

• Finding named entities

• Fine-tuning spaCy's models for your own custom applications

• Finding parts of speech and mapping the grammatical structure of sentences

• Using regular expressions

• Preparing text data for classification tasks

• Using techniques such as bag-of-words and TF-IDF for classification

• Modeling the topics present in a text with LDA

• Using pretrained word embeddings

• Building advanced models with the Keras functional API

• Training your model to focus on attention

• Translating sentences with the seq2seq model

You now have a big set of tools in your toolbox that will allow you to tackle NLP problems. Throughout the rest of this book, you will see some of these techniques again, being used in different contexts to solve hard problems. These techniques are useful across...