Text Analytics with Python: A Practitioner's Guide to Natural Language Processing (2 Ed.) | 誠品線上

Text Analytics with Python: A Practitioner's Guide to Natural Language Processing (2 Ed.)

內容簡介

內容簡介 Chapter 1: Natural Language BasicsChapter Goal: Introduces the readers to the basics of NLP and Text processingNo of pages: 40 - 50 Sub -Topics1. Language Syntax and Structure2. Text formats and grammars3. Lexical and Text Corpora resources4. Deep dive into the Wordnet corpus5. Parts of speech, Stemming and lemmatizationChapter 2: Python for Natural Language ProcessingChapter Goal: A useful chapter for people focusing on how to setup your own python environment for NLP and also some basics on handling text data with python and coverage of popular open source frameworks for NLPNo of pages: 20 - 30Sub - Topics 1. Setup Python for NLP2. Handling strings with Python3. Regular Expressions with Python4. Quick glance into nltk, gensim, spacy, scikit-learn, keras Chapter 3: Processing and Understanding TextChapter Goal: This chapter covers all the techniques and capabilities needed for processing and parsing text into easy to understand formats. We also look at how to segment and normalize text. No of pages: 35 - 40Sub - Topics: 1. Sentence and word tokenization2. Text tagging and chunking3. Text Parse Trees3. Text normalization4. Text spell checks and removal of redundant characters5. Synonyms and SynsetsChapter 4: Feature Engineering for Text DataChapter Goal: This chapter covers important strategies to extract meaningful features from unstructured text data. This includes traditional techniques as well as newer deep learning based methods. No of pages: 40 - 50Sub - Topics: 1. Feature engineering strategies for text data2. Bag of words model3. TF-IDF model3. Bag of N-grams model4. Topic Models5. Word Embedding based models (word2vec, glove)Chapter 5: Text ClassificationChapter Goal: Introduces readers to the concept of classification as a supervised machine learning problem and looks at a real world example for classifying text documentsNo of pages: 30 - 40Sub - Topics: 1. Classification basics2. Types of classifiers3. Feature generation of text documents4. Binary and multi-class classification models5. Building a text classifier on real world data with machine learning6. Some coverage of deep learning based classifiers7. Evaluating ClassifiersChapter 6: Text summarization and topic modelingChapter Goal: Introduces the concepts of text summarization, n-gram tagging analysis and topic models to the readers and looks at some real world datasets and hands-on implementations on the sameNo of pages: 40 - 45Sub - Topics: 1. Text summarization concepts2. Dimensionality reduction3. N-gram tagging models4. Topic modeling using LDA and LSA5. Generate topics from real world data6. N-gram analysis to generate patterns from app reviews (only if it performs well)7. Basics on deep learning for summarization Chapter 7: Text Clustering and Similarity analysisChapter Goal: We look at unsupervised machine learning concepts here like text clustering and similarity measuresNo of pages: 35 - 40Sub - Topics: 1. Clustering concepts2. Analyzing text similarity3. Implementing text similarity with cosine, jaccard meas

作者介紹

作者介紹 Dipanjan (DJ) Sarkar is a Data Scientist at Red Hat, a published author and a consultant and trainer. He has consulted and worked with several startups as well as Fortune 500 companies like Intel. He primarily works on leveraging data science, advanced analytics, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He is also an avid supporter of self-learning and massive open online courses. He has recently ventured into the world of open-source products to improve the productivity of developers across the world. Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. He also acts as a key contributor and Editor for Towards Data Science, a leading online journal focusing on Artificial Intelligence and Data Science. Dipanjan has also authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing and Deep Learning. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science, artificial intelligence and deep learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https: medium.com @dipanzan.sarkar and https: www.linkedin.com in dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https: github.com dipanjanS.

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