Thank you for choosing Applied Natural Language Processing with Python
for your journey into natural language processing (NLP). Readers should
be aware that this text should not be considered a comprehensive study
of machine learning, deep learning, or computer programming. As such,
it is assumed that you are familiar with these techniques to some degree.
Regardless, a brief review of the concepts necessary to understand the
tasks that you will perform in the book is provided.
After the brief review, we begin by examining how to work with raw
text data, slowly working our way through how to present data to machine
learning and deep learning algorithms. After you are familiar with some
basic preprocessing algorithms, we will make our way into some of the
more advanced NLP tasks, such as training and working with trained
word embeddings, spell-check, text generation, and question-and-answer
generation.
All of the examples utilize the Python programming language and
popular deep learning and machine learning frameworks, such as scikit-
learn, Keras, and TensorFlow. Readers can feel free to access the source
code utilized in this book on the corresponding GitHub page and/or try
their own methods for solving the various problems tackled in this book
with the datasets provided.
