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