Write Code That Understands Human Language
Natural language processing sits at the intersection of linguistics and programming. Our courses teach you how machines can parse, interpret, and generate text that people actually use. No fluff, just practical techniques that work in real applications.
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Built Around Real Projects
You won't spend weeks on theory before touching code. We start with a working chatbot on day one. Then we break it apart to understand how each piece functions.
By week three, most students have built their first text classifier. Week six brings sentiment analysis tools. The progression follows what you'll actually encounter when building NLP systems professionally.
Text Processing
Tokenization, stemming, and parsing techniques that prepare raw text for analysis
Model Training
Hands-on work with transformer models and fine-tuning pre-trained systems
API Integration
Connect your NLP models to web services and production environments
Performance Testing
Measure accuracy, optimize response times, and handle edge cases effectively
Three Stages of Development
Most students move through our program in eight to twelve months. The pace depends on your background and how much time you can dedicate each week.
Foundation Skills
Python fundamentals, regex patterns, basic text manipulation, and introduction to libraries like NLTK and spaCy
Core Techniques
Named entity recognition, part-of-speech tagging, language models, and working with vector embeddings
Advanced Applications
Fine-tuning transformers, building custom models, deployment strategies, and scaling solutions
Technologies You'll Master
Our curriculum centers on Python because that's what the industry uses. But the concepts transfer. Once you understand how attention mechanisms work in transformers, you can apply that knowledge across different frameworks.
We focus on tools with strong community support and regular updates. That means you're learning skills that remain relevant as the field evolves.
Programs starting September 2025
Learn From Thorsten Maartens
Lead Instructor
Thorsten spent seven years building language processing systems for search engines before switching to teaching. He still consults on NLP projects, which keeps the coursework grounded in current practices rather than outdated textbook examples.
His approach emphasizes debugging skills and understanding why models fail. Because in real work, you spend more time fixing problems than celebrating successes.
Meet the Full Team