Do you have an interest in starting your own natural language processing project, but feel overwhelmed by all the talk of attention-based models and text embeddings? Would you like to understand how you can transform a set of texts into features for a model? In this talk, I'll give you a practical demonstration of how meaningful features are created from text data, going from the simplest approaches and working up to cutting edge techniques such as BERT. I’ll demonstrate how to do this using some of the most popular Python packages for NLP, including scikit-learn, nltk, gensim and transformers. At each step, we'll discuss why each technique works, what meaning it extracts from the text and what it leaves behind, and the advantages and disadvantages of using each.