Description
Book SynopsisIntermediate user level
Table of ContentsChapter 1: Extracting the Data
Chapter Goal: Understanding the potential data sources to build NLP applications for business benefits and ways to extract the text data with examples
No of pages: 23
Sub - Topics:
1. Data extraction through API
2. Reading HTML page, HTML parsing
3. Reading pdf file in python
4. Reading word document
5. Regular expressions using python
6. Handling strings using python
7. Web scraping
Chapter 2: Exploring and Processing the Text Data
Chapter Goal: Data is never clean. This chapter will give in depth knowledge about how to clean and process the text data. It covers topics like cleaning, tokenizing and normalizing text data.
No of pages: 22
Sub - Topics
1 Text preprocessing methods
2 Data cleaning – punctuation removal, stopwords removal, spelling correction
3 Lexicon normalization – stemming and lemmatization
4 Tokenization
5 Dealing with emoticons and emojis
6 Exploratory data analysis
7 End to end text processing pipeline implementation
Chapter 3: Text to Features
Chapter Goal: 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 (basic to advanced).
No of pages: 40
Sub - Topics
1 One hot encoding
2 Count vectorizer
3 N grams
4 Co-occurrence matrix
5 Hashing vectorizer
6 TF-IDF
7 Word Embedding - Word2vec, fasttext
8 Glove embeddings
9 ELMo
10 Universal Sentence Encoder
11 Understanding Transformers like BERT, GPT
12 Open AIs
Chapter 4: Implementing Advanced NLP
Chapter Goal: Understanding and building advanced NLP techniques to solve the business problems starting from text similarity to speech recognition and language translation.
No of pages: 25
Sub - Topics:
1. Noun phrase extraction
2. Text similarity
3. Parts of speech tagging
4. Information extraction – NER – entity recognition
5. Topic modeling
6. Machine learning for NLP –
a. Text classification
7. Sentiment analysis
8. Word sense disambiguation
9. Speech recognition and speech to text
10. Text to speech
11. Language detection and translation
Chapter 5: Deep Learning for NLP
Chapter Goal: Unlocking the power of deep learning on text data. Solving few real-time applications of deep learning in NLP.
No of pages: 55
Sub - Topics:
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.
6. Sentence comparison using SentenceBERT
7. Understanding GPT
8. Comparison between BERT, RoBERTa, DistilBERT, XLNet
Chapter 6: Industrial Application with End to End Implementation
Chapter Goal: 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 pages: 90
Sub - Topics:
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. Product360 - Sentiment, emotion & trend capturing system
7. TED Talks segmentation & topics extraction using machine learning
8. Fake news detection system using deep neural networks
9. E-commerce search engine & recommendation systems using deep learning
10. Movie genre tagging using multi-label classification
11. E-commerce product categorization using deep learning
12. Sarcasm detection model using CNN
13. Building chatbot using transfer learning
14. Summarization system using RNN and reinforcement learning
Chapter 7: Conclusion - Next Gen NLP & AI
Chapter Goal: So far, we learnt how NLP when coupled with machine learning and deep learning helps us solve some of the complex business problems across industries and domains. In this chapter let us uncover how some of the next generation algorithms that would potentially play important roles in the future NLP era.
