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Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python
Chapter 1: Extracting the data Chapter Understanding the potential data sources to build natural language processing applications for business benefits and ways to extract the data with examples No of 20 Sub -
1. Data extraction through API 2. Web scraping 3. Regular expressions 4. Handling strings
Chapter 2: Exploring and processing text data Chapter Data is never clean. This chapter will give in depth knowledge about how to clean and process the text data. It also cover tokenizing and parsing. No of 15 Sub - Topics 1. Text preprocessing methods using python 1. Data cleaning 2. Lexicon normalization 3. Tokenization 4. Parsing and regular expressions 5. Exploratory data analysis
Chapter 3: Text to features Chapter One of the important task with text data is to transform text data into machines or algorithms understandable form, by using different feature engineering methods No of 20 Sub - Topics 1. Feature engineering using python o One hot encoding o Count vectorizer o TF-IDF o Word2vec o N grams
Chapter 4: Advanced natural language processing Chapter A comprehensive understanding of key concepts, methodologies and implementation of natural language processing techniques. No of 40 Sub - 1. Text similarity 2. Information extraction - NER 3. Topic modeling 4. Machine learning for NLP - a. Text classification b. Sentiment Analysis 5. Deep learning for NLP- a. Seq2seq, b. Sequence prediction using LSTM and RNN 6. Summarizing text
Chapter 5: Industrial application with end to end implementation Chapter Solving real time NLP applications with end to end implementation using python. Right from framing and understanding the business problem to deploying the model. No of 40 Sub - 1. Consumer complaint classification 2. Customer reviews sentiment prediction 3. Data stitching using text similarity and record linkage 4. Text summarization for subject notes 5. Document clustering 6. Architectural details of Chatbot and Search Engine along with Learning to rank
Chapter 6: Deep learning for NLP Chapter Unlocking the power of deep learning on text data. Solving few real-time applications of deep learning in NLP. No of 40 Sub - 1. Fundamentals of deep learning 2. Information retrieval using word embedding's 3. Text classification using deep learning approaches (CNN, RNN, LSTM, Bi-directional LSTM) 4. Natural language generation - prediction next word/ sequence of words using LSTM. 5. Text summarization using LSTM encoder and decoder.
1. Data extraction through API 2. Web scraping 3. Regular expressions 4. Handling strings
Chapter 2: Exploring and processing text data Chapter Data is never clean. This chapter will give in depth knowledge about how to clean and process the text data. It also cover tokenizing and parsing. No of 15 Sub - Topics 1. Text preprocessing methods using python 1. Data cleaning 2. Lexicon normalization 3. Tokenization 4. Parsing and regular expressions 5. Exploratory data analysis
Chapter 3: Text to features Chapter One of the important task with text data is to transform text data into machines or algorithms understandable form, by using different feature engineering methods No of 20 Sub - Topics 1. Feature engineering using python o One hot encoding o Count vectorizer o TF-IDF o Word2vec o N grams
Chapter 4: Advanced natural language processing Chapter A comprehensive understanding of key concepts, methodologies and implementation of natural language processing techniques. No of 40 Sub - 1. Text similarity 2. Information extraction - NER 3. Topic modeling 4. Machine learning for NLP - a. Text classification b. Sentiment Analysis 5. Deep learning for NLP- a. Seq2seq, b. Sequence prediction using LSTM and RNN 6. Summarizing text
Chapter 5: Industrial application with end to end implementation Chapter Solving real time NLP applications with end to end implementation using python. Right from framing and understanding the business problem to deploying the model. No of 40 Sub - 1. Consumer complaint classification 2. Customer reviews sentiment prediction 3. Data stitching using text similarity and record linkage 4. Text summarization for subject notes 5. Document clustering 6. Architectural details of Chatbot and Search Engine along with Learning to rank
Chapter 6: Deep learning for NLP Chapter Unlocking the power of deep learning on text data. Solving few real-time applications of deep learning in NLP. No of 40 Sub - 1. Fundamentals of deep learning 2. Information retrieval using word embedding's 3. Text classification using deep learning approaches (CNN, RNN, LSTM, Bi-directional LSTM) 4. Natural language generation - prediction next word/ sequence of words using LSTM. 5. Text summarization using LSTM encoder and decoder.
262 pages, Paperback
Published January 30, 2019
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