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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.

What You Will Learn

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
Who This Book Is For
You should be at least a beginner in ML to get the most out of this text, but you needn´t feel that you need be an expert to understand the content.

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Chapter 1: What is Natural Language Processing? Chapter Goal: Establishing understanding of topic and give overview of text No of pages: 10 pages Sub -Topics 1. History of Natural Language Processing 2. Word Embeddings 3. Neural Networks applied to Natural Language Processing 4. Python Packages

Chapter 2: Review of Machine Learning Chapter Goal: Discuss models that will be referenced in the text No of pages: 30 pages Sub - Topics 1. Gradient Descent 2. Multi-Layer Perceptrons 3. Recurrent Neural Networks 4. 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: 30 Sub - Topics: 1. Word Tokenization 2. Preprocessing and cleaning of text data 3. Web crawling w/ SpaCy 4. Lemmas, N-grams, and other NATURAL LANGUAGE PROCESSING concepts
Chapter 4: Word Embeddings and their application Chapter Goal: Introduce reader to the use cases for word embeddings and the packages we utilize for them No of pages: 50 Sub - Topics: 1. Word2Vec 2. Doc2Vec 3. GloVe
Chapter 5: Using Machine Learning w/ Natural language Processing Chapter 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: 50 1. Tensorflow 2. Keras 3. Caffe

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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. 

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