Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing is a practical, hands-on guide to building real-world NLP applications using Python.
Written by Taweh Beysolow II and published by Apress, this book bridges the gap between theory and practice. It begins with a solid review of essential machine learning concepts before diving into core natural language processing techniques.
You’ll learn how to:
- Preprocess and manipulate raw text data (including .txt and .pdf files)
- Perform tasks like spell checking, text summarization, document classification, topic modeling, and text generation
- Work with powerful libraries such as NLTK, Gensim, SpaCy, TensorFlow, and Keras
- Implement advanced methods including Word2Vec, Doc2Vec, and recurrent neural networks for language modeling and machine translation
Ideal for data scientists, machine learning practitioners, Python developers, and students with beginner-to-intermediate ML knowledge, this concise yet comprehensive book (first edition, 2018) focuses on practical implementation so you can immediately apply these techniques in professional environments — from automating customer support to analyzing large text corpora.
By the end, you’ll have the skills to harness AI for natural language understanding and build scalable NLP solutions.