Named Entity Recognition Courses and Certifications

Center for Language & Speech Processing(CLSP), JHU

Tutorial on Information Extraction - Part 1

Discover information extraction fundamentals with BBN Technologies expert Ralph Weischedel in this comprehensive tutorial covering key concepts and methodologies.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Named Entity Classification in the Biology Domain

Explore named entity classification techniques specifically designed for biological text processing and domain-specific language understanding challenges.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

1littlecoder

Custom NER with spaCy v3 Tutorial - Named Entity Recognition Tutorial

Learn to build custom Named Entity Recognition models using spaCy v3, including data annotation with free tools and training techniques for improved NLP applications.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Introduction to Natural Language Processing in Python

40 ratings at DataCamp

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Feature Engineering for NLP in Python

13 ratings at DataCamp

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Spoken Language Processing in Python

Learn how to load, transform, and transcribe speech from raw audio files in Python.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Natural Language Processing with spaCy

Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Python Tutorials for Digital Humanities

Named Entity Recognition Tools in spaCy - A Comparison Guide

Master the differences between spaCy's entity recognition tools and learn when to effectively implement NER, EntityRuler, SpanCat, and SpanRuler for optimal text processing results.

• YouTube
• 11 minutes
• On-Demand
• Free Video

Microcredential

Token Classification in NLP using spaCy

7 ratings at CodeSignal

Master token classification techniques using spaCy, from building NLP pipelines to implementing practical applications. Learn linguistics fundamentals and apply your skills to real-world case studies.

• CodeSignal
• 7 hours
• On-Demand
• Free Certificate
• 3 courses

Collecting and Preparing Textual Data for Classification

Master essential text data collection and preprocessing techniques for NLP, covering data cleaning, stop word removal, stemming, n-gram generation, and named entity recognition for effective classification projects.

• CodeSignal
• 5 hours
• On-Demand
• Free Certificate

Linguistics for Token Classification in spaCy

Master linguistic concepts and semantic analysis for token classification using spaCy, from syntactic dependencies to custom NLP pipeline components and entity recognition techniques.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Practical Applications of spaCy for Real-Life Tasks

Master practical spaCy applications through hands-on case studies, from spam detection and entity recognition to legal document analysis - all using real-world scenarios.

• CodeSignal
• 1 hour
• On-Demand
• Free Certificate

Natural Language Processing: NLP With Transformers in Python

2582 ratings at Udemy

Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more

• Udemy
• 11 hours 31 minutes
• On-Demand
• Paid Course

Intro to Natural Language Processing (NLP) in Python for AI

1320 ratings at Udemy

Learn the NLP Technology Behind AI Tools Like ChatGPT: Understanding, Generating, and Classifying Human Language

• Udemy
• 3 hours 41 minutes
• On-Demand
• Paid Course

Natural Language Processing with Sequence Models

1180 ratings at Coursera

Train neural networks for sentiment analysis, text generation, named entity recognition, and question comparison using advanced NLP techniques like GLoVe embeddings, GRUs, LSTMs, and Siamese models.

• Coursera
• 21 hours 6 minutes
• On-Demand
• Paid Course