Natural Language Processing Immersion
Build real language models, work with production datasets, and understand how machines actually interpret human communication. This isn't theory from textbooks—it's hands-on work with the same tools used by research teams and tech companies.
Common Roadblocks We Address
Most people hit the same walls when learning NLP. Here's what actually trips people up and how we handle each challenge in practical terms.
Math Anxiety Meets Machine Learning
Linear algebra and probability sound scary. You open a textbook, see pages of notation, and your brain shuts down. Suddenly transformers aren't just confusing—they're impossible.
Our ApproachWe start with code first. You'll see what attention mechanisms do before diving into matrix multiplication. The math makes sense once you've already built something that works. Most students tell us this backwards approach finally made statistics click.
Dataset Nightmares
Real text data is messy. Emojis break your tokenizer. One dataset has labels in Chinese. Another has inconsistent formatting. Your model learns nothing because your preprocessing was wrong three steps ago.
Our ApproachWe spend two full weeks on data wrangling. You'll work with deliberately problematic datasets—Twitter dumps with weird encodings, scraped forums with HTML fragments, customer reviews in mixed languages. By week three, broken data won't scare you anymore.
Model Training Black Box
Your loss function flatlines after epoch two. Is it the learning rate? Dead neurons? Bad data? You change random hyperparameters hoping something works. Six hours later, you're more confused than when you started.
Our ApproachEvery student builds a debugging toolkit during week five. You'll learn to visualize attention weights, track gradient flow, and interpret validation curves. We break models on purpose so you recognize failure patterns. Troubleshooting becomes methodical instead of guesswork.
Production Reality Gap
Your notebook works perfectly with test data. But deploying to production? Latency spikes. Memory explodes. Edge cases you never considered break everything. Suddenly your 95% accuracy model is worthless in the real world.
Our ApproachThe final project requires deployment. You'll containerize your model, set up monitoring, handle concurrent requests, and write fallback logic. We simulate production stress—API timeouts, malformed inputs, server crashes. If it can't survive week thirteen, it doesn't pass.
Learning Through Real Problems
Most courses give you clean datasets and ignore the hard parts. We do the opposite. You'll work with actual scraped data, deal with encoding issues, and figure out why your model memorized the training set.
By week eight, you're not following tutorials anymore. You're making architectural decisions, debugging subtle issues, and explaining tradeoffs like someone who's been doing this for years.
The projects aren't polished demos. They're messy, functional systems that taught you something valuable even when they didn't work the first five times.
Who's Teaching This Program
Torstein Viklund
Lead InstructorSpent six years building search systems at a Taiwan fintech company. He's the person who figures out why models fail in production and then teaches you to spot the same patterns.
- Transformer architectures
- Production deployment
- Cross-lingual models
Saskia Hendriks
Research Track LeadPublished three papers on low-resource language processing. She makes academic concepts actually usable and helps students understand what's worth implementing versus what's just clever theory.
- Research methodology
- Linguistic features
- Model evaluation
Anneliese Bergström
Engineering MentorBuilds chatbots and content moderation systems. She's seen every possible way text processing can break and teaches you to write defensive code that handles real user input.
- API design
- System scaling
- Real-time processing
How The Program Builds
We start with practical implementation and add complexity each phase. By the end, you're making architectural decisions and understanding why certain approaches work better than others.
Text Processing Fundamentals
You'll write tokenizers from scratch, handle different encodings, and build your own text preprocessing pipeline. No libraries hiding the details—just you and messy strings until it clicks.
Classical NLP Techniques
Before jumping to transformers, you need to understand what came before. TF-IDF, n-grams, and basic classifiers teach you why modern architectures make the choices they do.
Neural Architecture Deep Dive
This is where it gets interesting. You'll implement attention from scratch, understand why positional encodings matter, and train your first transformer on real data. Expect things to break—that's when learning happens.
Production Systems
Your final project goes live. You'll handle real traffic, monitor performance, and fix issues as they come up. This phase separates people who can build demos from those who ship working systems.
September 2025 Cohort
Applications open in May. We're looking for people who've written code before and want to understand how language models actually work. No ML background required, but you should be comfortable reading Python and debugging your own problems.
Program Start
September 8, 2025
Application Deadline
July 15, 2025
Time Commitment
20 hours per week
Format
Hybrid (in-person + remote)
What Happens After
Graduates are working at research labs, building chatbots for startups, or improving search systems at larger companies. Some went back to their existing jobs with new skills. Others switched careers entirely.
We don't promise job placements because that's dishonest. What we do promise is that you'll understand NLP deeply enough to have informed conversations with researchers and make architectural decisions that don't fall apart under load.
The Taiwan tech community is small. People who do solid work here get noticed. That's been true for every cohort so far.
