Lilly Tech Systems
Learn spaCy
Master industrial-strength Natural Language Processing with spaCy. Build NLP pipelines, extract entities, classify text, and deploy production-ready language models — fast and efficiently.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is spaCy, how it compares to NLTK and Transformers, and why it's built for production NLP.
2. Installation
Install spaCy, download language models, and configure GPU support for faster processing.
3. NLP Pipeline
Understand tokenization, POS tagging, dependency parsing, lemmatization, and the spaCy pipeline architecture.
4. Named Entity Recognition
Extract people, organizations, dates, and custom entities. Train custom NER models on your data.
5. Text Classification
Build text classifiers for sentiment analysis, spam detection, and topic categorization with spaCy.
6. Best Practices
Production deployment, custom components, performance tuning, and scaling spaCy pipelines.
What You'll Learn
By the end of this course, you'll be able to:
Process Text
Tokenize, parse, and analyze text at production speed using spaCy's optimized Cython pipeline.
Extract Entities
Identify people, places, organizations, and custom entities from unstructured text automatically.
Classify Text
Build and train text classification models for sentiment, intent, and topic categorization.
Deploy NLP
Package spaCy models for production with proper versioning, testing, and performance monitoring.