Synopses & Reviews
Synopsis
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
Synopsis
Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing, users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.
This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.
- Extract data from APIs and web pages
- Prepare textual data for statistical analysis and machine learning
- Use machine learning for classification, topic modeling, and summarization
- Explain AI models and classification results
- Explore and visualize semantic similarities with word embeddings
- Identify customer sentiment in product reviews
- Create a knowledge graph based on named entities and their relations