Step-by-step solutions for text extraction, preprocessing, and analysis enable efficient text data preparation for machine learning
Detailed code examples in Python demonstrate best practices for classification, topic modeling, and sentiment analysis
Case studies on building knowledge graphs and visualizing word embeddings provide hands-on learning for advanced NLP tasks
Explanations of AI model interpretation and evaluation help users effectively assess and improve their NLP models
Comprehensive coverage of API data extraction and web scraping techniques supports integration of external data sources
Summarized by Shop
Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), 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
Author
Jens Albrecht, Sidharth Ramachandran, Christian Winkler