Natural Language Processing Recipes : Unlocking Text Data with Machine Learning and Deep Learning using Python /: Unlocking Text Data with Machine Learning and Deep Learning using Python. ([2019])

• Record Type: Book
• Further Information: Note: Akshay Kulkarni, Adarsha Shivananda.
• Authors: Kulkarni, Akshay; Shivananda, Adarsha
• Contents: Chapter 1: Extracting the dataChapter Goal: Understanding the potential data sources to build natural language processing applications for business benefits and ways to extract the data with examplesNo of pages: 20Sub - Topics: 1. Data extraction through API2. Web scraping 3. Regular expressions4. Handling strings Chapter 2: Exploring and processing text dataChapter Goal: 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 pages: 15Sub - Topics1. Text preprocessing methods using python1. Data cleaning2. Lexicon normalization3. Tokenization 4. Parsing and regular expressions5. Exploratory data analysis Chapter 3: Text to featuresChapter 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 No of pages: 20Sub - Topics1. Feature engineering using pythono One hot encodingo Count vectorizero TF-IDFo Word2veco N grams Chapter 4: Advanced natural language processingChapter Goal: A comprehensive understanding of key concepts, methodologies and implementation of natural language processing techniques.No of pages: 40Sub - Topics: 1. Text similarity2. Information extraction – NER3. Topic modeling4. Machine learning for NLP – a. Text classificationb. Sentiment Analysis5. Deep learning for NLP-a. Seq2seq, b. Sequence prediction using LSTM and RNN6. Summarizing text Chapter 5: 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: 40Sub - 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. Architectural details of Chatbot and Search Engine along with Learning to rank Chapter 6: Deep learning for NLPChapter Goal: Unlocking the power of deep learning on text data. Solving few real-time applications of deep learning in NLP.No of pages: 40Sub - Topics: 1. Fundamentals of deep learning2. 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.
• Publisher Details: New York : Springer Science Standard Apress
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 005.133; COMPUTERS / Programming Languages / Python; Python (Computer program language); Natural language processing (Computer science); Electronic books
• Languages: English
• ISBNs: 9781484242674
• Related ISBNs: 148424267X; 9781484242667
• Notes: Note: Vendor-supplied metadata.; Note: Online resource; title from PDF title page (EBSCO, viewed January 31, 2019).
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