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Applied Natural Language Processing with Python
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Table of Contents

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

About the Author

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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