Video description
Whether you’re a programmer with little to no knowledge of Python, or an experienced data scientist or engineer, this course 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.
You’ll learn the most common techniques for processing text, how to use machine learning to generate annotators and apply them within a data pipeline, and the differences between NLP pipelines and other approaches to semantic text mining. You’ll learn about standard UIMA annotators, custom annotators, and machine-learned annotators, and understand how architectures for text processing pipelines can incorporate some of the most popular big data tools such as Kafka, Spark, SparkSQL, Cassandra, and ElasticSearch.
By the end of the course, you will be able to build a natural language processing and entity extraction pipeline, and will have a complete understanding of the capabilities and limitations of natural language text processing.
Materials or downloads needed in advance: Example files
Table of contents
- Introduction
- Getting Started: Basic String Processing In Python
- Converting Text To Symbols: Tokenization In NLTK and spaCy
- Going Subsymbolic: Vector Representations
- Finding The Structure Of Text: Parsing In spaCy
- Determining How The Writer Feels: Sentiment Analysis In VADER
- Making Decisions: Text Classification
- Indentifying Discussed Topics: LDA In Gensim
- Toward Machine Reading: Entity Extraction And Linking
- Conclusion
- Part 1: Introduction
- Part 2: NLP Pipelines
- Part 3 - Annotators
- Part 4: Custom Annotators
- Part 5: Machine Learned Annotators
- Part 6: Ontology Enrichment
- Part 7: Architecture
- Part 8: Parting Advice
- Part 1: Building a natural language processing and entity extraction pipeline on Scala Spark
- Part 2: Machine Learning Applications for Statistical Natural Language Understanding at Scale
- Part 3: Topic Modeling on Natural Language with Scala, Spark and MLLib
- Part 4: Deep Learning Applications for Natural Language Understanding with Scala, Spark and MLLib
Product information
- Title: Get Started with Natural Language Processing Using Python, Spark, and Scala
- Author(s):
- Release date: March 2017
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491985847
You might also like
book
Hands-On Python Natural Language Processing
Get well-versed with traditional as well as modern natural language processing concepts and techniques Key Features …
video
Natural Language Text Processing with Python
Even though computers can't read, they're very effective at extracting information from natural language text. They …
book
Interpretable Machine Learning with Python
A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete …
book
Natural Language Processing with Spark NLP
If you want to build an enterprise-quality application that uses natural language text but aren’t sure …