Albrecht J Blueprints for Text Analytics Using Python 2020 torrent

Textbook in PDF format

Turning text into valuable information is essential for many businesses looking to gain a competitive advantage. There have many improvements in natural language processing and users have a lot of options when choosing to work on a problem. However, it’s not always clear which NLP tools or libraries would work for a business use—or which techniques you should use and in what order.
This practical book provides theoretical background and real-world case studies with detailed code examples to help developers and data scientists obtain insight from text online. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler use blueprints for text-related problems that apply state-of-the-art machine learning methods in Python.
If you have a fundamental understanding of statistics and machine learning along with basic programming experience in Python, you’re ready to get started. You’ll learn how to:
Crawl and clean then explore and visualize textual data in different formats
Preprocess and vectorize text for machine learning
Apply methods for classification, topic analysis, summarization, and knowledge extraction
Use semantic word embeddings and deep learning approaches for complex problems
Work with Python NLP libraries like spaCy, NLTK, and Gensim in combination with scikit-learn, Pandas, and PyTorch
How to Gain First Insights from Textual Data
What You’ll Learn and What We Will Build
Exploratory Data Analysis
Introducing the Dataset
Blueprint: Building a Simple Text Preprocessing Pipeline
Analyzing Word Frequencies
Blueprint: Finding a Keyword in Context (KWIC)
Blueprint: Analyzing N-Grams
Blueprint: Comparing Frequencies across Time-Intervals and Categories
Closing Remarks
Scraping Websites and Extracting Data
What You’ll Learn and What We Will Build
Scraping and Data Extraction
Introducing the Reuters News Archive
URL Generation
Downloading Data
Extracting Semi-structured Data
Blueprint: Spidering
Density-based Text Extraction
All-in-one Approach
Possible Problems with Scraping
Closing Remarks and Recommendation