SpaCy, a fast, user-friendly library for teaching computers to understand text, simplifies NLP techniques, such as speech tagging and syntactic dependencies, so you can easily extract information, attributes, and objects from massive amounts of text to then document, measure, and analyze. This Learning Path is a hands-on introduction to using SpaCy to discover insights through natural language processing. While end-to-end natural language processing solutions can be complex, you’ll learn the linguistics, algorithms, and machine learning skills to get the job done.
Whether you’re a programmer with little to no knowledge of Python, or an experienced data scientist or engineer, this Learning Path will walk you through natural language processing, using both Python and Scala, and show you how to implement a range of popular tools including Spark, scikit-learn, SpaCy, NLTK, and gensim for text mining.
Even though computers can't read, they're very effective at extracting information from natural language text. They can determine the main themes in the text, figure out if the writers of the text have positive or negative feelings about what they've written, decide if two documents are similar, add labels to documents, and more.
Spring Integration and Spring Batch make it easy to create enterprise integration solutions and batch applications with minimal fuss. Learn the Spring approach to development as you explore the fundamentals that drive these powerful frameworks. By the end of this Learning Path, you’ll be able to enable lightweight messaging within Spring-based applications, support integration with external systems via declarative adapters, and process large volumes of records, including logging and tracing, transaction management, and job processing statistics.