"Applied Natural Language Processing with Python" by Taweh Beysolow II is a practical guide that demonstrates how to implement natural language processing (NLP) algorithms using Python. The book covers a range of topics from traditional NLP techniques such as tokenization, stemming, and part-of-speech tagging, to modern deep learning techniques such as word embeddings, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
The book begins with an introduction to NLP and Python programming, followed by an overview of the various NLP tasks such as text classification, sentiment analysis, and topic modeling. The author then dives into the details of implementing these tasks using Python and popular NLP libraries such as NLTK, spaCy, and Gensim.
One of the strengths of the book is its focus on practical examples. The author presents real-world NLP problems and shows how to solve them using Python and machine learning techniques. For instance, the book shows how to classify movie reviews as positive or negative using various classifiers such as logistic regression, support vector machines (SVMs), and random forests. It also shows how to use deep learning techniques such as CNNs and RNNs to classify text.
Overall, "Applied Natural Language Processing with Python" is a valuable resource for anyone who wants to learn NLP and how to implement NLP algorithms using Python. The book assumes a basic knowledge of Python programming and machine learning concepts, making it accessible to both beginners and advanced readers.
