Applied Natural Language Processing with Python

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. You will:

• Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim
• Manipulate and preprocess raw text data in formats such as .txt and .pdf
• Strengthen your skills in data science by learning both the theory and the application of various algorithms

Auteur
Taweh Beysolow II is a Machine Learning Scientist and Author currently based in the United States. He has a Bachelor of Science degree in Economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. His professional experience has included applying machine learning and natural language processing techniques to financial, text (structured and unstructured), and social media data.

Contenu
Chapter 1: What is Natural Language Processing? Chapter Goal: Establishing understanding of topic and give overview of textNo of pages: 10 pagesSub -Topics1. History of Natural Language Processing 2. Word Embeddings3. Neural Networks applied to Natural Language Processing 4. Python Packages

Chapter 2: Review of Machine LearningChapter Goal: Discuss models that will be referenced in the textNo of pages: 30 pagesSub - Topics 1. Gradient Descent 2. Multi-Layer Perceptrons 3. Recurrent Neural Networks4. LSTM networks
Chapter 3: Working with Raw Text Chapter Goal: Introduce reader to the fundamental aspects of Natural Language Processing that will be utilized more heavily in the chapters regarding No of pages: 30Sub - Topics: 1. Word Tokenization 2. Preprocessing and cleaning of text data3. Web crawling w/ SpaCy4. Lemmas, N-grams, and other NATURAL LANGUAGE PROCESSING concepts
Chapter 4: Word Embeddings and their applicationChapter Goal: Introduce reader to the use cases for word embeddings and the packages we utilize for themNo of pages: 50 Sub - Topics: 1. Word2Vec2. Doc2Vec3. GloVe
Chapter 5: Using Machine Learning w/ Natural language ProcessingChapter Goal: Give reader specific walkthroughs of advanced applications of Natural Language Processing using Machine Learning within greater applications (spellcheck and sentiment analysis)No of pages: 501. Tensorflow2. Keras3. Caffe