From print('hello') to GPT — in 12 weeks.
Stop calling APIs you don't understand. Learn to tokenize, vectorize, fine-tune, and deploy — from first principles to production transformers.
Every week,
one abstraction deeper.
// TOKENIZE // EMBED // ATTEND // GENERATE
Regex, Tokenization & the Vocabulary Problem
Every model starts here
- Rule-based tokenizers
- BPE & WordPiece
- Unicode edge cases
- Building vocab from scratch
Word Vectors & Semantic Space
Meaning as geometry
- Word2Vec skip-gram
- GloVe co-occurrence
- Cosine similarity
- Bias in embeddings
king − man + woman ≈ queen · Semantic arithmetic in vector space
Attention Mechanisms & Self-Attention
The architecture that changed everything
- Scaled dot-product attention
- Multi-head attention
- Positional encoding
- Visualizing attention
BERT, GPT & the Transformer Family
Pre-training and fine-tuning in practice
- Masked language modeling
- Causal language modeling
- Fine-tuning on custom data
- LoRA & PEFT
Named Entity Recognition & Information Extraction
Making models answer specific questions
- Sequence labeling
- spaCy pipelines
- Relation extraction
- Building domain NER
Capstone: Build a Production Chatbot
Ship something real
- RAG pipeline design
- Vector database integration
- Evaluation metrics
- Deployment on Hugging Face
Three paths in.
One destination.
You call the API. You never see inside.
You've shipped GPT-powered features. You can't explain why they fail. You want to own the stack.
After 12 weeks: fine-tune your own model, debug tokenization bugs, build RAG pipelines from scratch.
"Can you make it understand emails?"
You're great with SQL and Pandas. Every stakeholder wants NLP. You're tired of saying 'I'll look into it.'
After 12 weeks: classify support tickets, extract entities from contracts, summarize reports automatically.
Your syntax trees have market value.
You understand language better than any ML engineer. You just don't know how to turn that into code.
After 12 weeks: build domain-specific NER, contribute to model evaluation, consult on language quality.
Pivoting into ML from finance, healthcare, or law? We have alumni who made the switch with zero CS degree. The course is self-paced within each cohort — you set the hours.
Real engineers.
Real outcomes.
“I used to copy-paste from the OpenAI docs and hope for the best. Now I debug tokenization issues before they reach production. The week on BPE alone was worth the tuition.”
“I have a linguistics PhD and everyone kept telling me to 'just learn Python.' ParseNLP showed me my syntax knowledge was actually a superpower — I got hired as an NLP researcher within 3 months.”
“I studied at 10pm after the kids were in bed. The Discord community kept me going — there's something about seeing 'someone's kid just interrupted their study session' and 200 people reacting with 😂 that makes it feel human.”
“The attention heatmap exercise in Week 6 broke my brain in the best way. I finally understood why BERT works. No other course gave me that moment.”
“My manager asked me to 'add NLP to the pipeline' without any spec. Six months ago I'd have panicked. Now I scoped it, built it, and shipped it in two sprints.”
“I used to copy-paste from the OpenAI docs and hope for the best. Now I debug tokenization issues before they reach production. The week on BPE alone was worth the tuition.”
“I have a linguistics PhD and everyone kept telling me to 'just learn Python.' ParseNLP showed me my syntax knowledge was actually a superpower — I got hired as an NLP researcher within 3 months.”
“I studied at 10pm after the kids were in bed. The Discord community kept me going — there's something about seeing 'someone's kid just interrupted their study session' and 200 people reacting with 😂 that makes it feel human.”
“The attention heatmap exercise in Week 6 broke my brain in the best way. I finally understood why BERT works. No other course gave me that moment.”
“My manager asked me to 'add NLP to the pipeline' without any spec. Six months ago I'd have panicked. Now I scoped it, built it, and shipped it in two sprints.”
“The capstone chatbot I built is now live in production at my company. I demo'd it in my performance review and got promoted. I could not have done that without this course.”
“I'm a 52-year-old analyst who thought transformers were only for 25-year-olds with CS degrees. The instructors genuinely meet you where you are. Best learning investment of my career.”
“Week 9 on NER is worth the whole course if you work in legal or finance. I built a contract extraction tool that saves my team 6 hours a week. Every week.”
“The Discord screenshot of the Zoom call where someone's toddler walked in mid-lecture and everyone sent heart emojis — that's the vibe of this whole course. Rigorous but warm.”
“I failed the first regex assignment. I almost dropped out. My cohort lead messaged me directly at 11pm. I finished top 10%. The human support is unlike any MOOC.”
“The capstone chatbot I built is now live in production at my company. I demo'd it in my performance review and got promoted. I could not have done that without this course.”
“I'm a 52-year-old analyst who thought transformers were only for 25-year-olds with CS degrees. The instructors genuinely meet you where you are. Best learning investment of my career.”
“Week 9 on NER is worth the whole course if you work in legal or finance. I built a contract extraction tool that saves my team 6 hours a week. Every week.”
“The Discord screenshot of the Zoom call where someone's toddler walked in mid-lecture and everyone sent heart emojis — that's the vibe of this whole course. Rigorous but warm.”
“I failed the first regex assignment. I almost dropped out. My cohort lead messaged me directly at 11pm. I finished top 10%. The human support is unlike any MOOC.”
Reserve Your Seat.
No payment today.
Just your spot in the room. Payment opens 7 days before cohort start — after you've attended the free orientation session.
Reserve Your Seat
Next Cohort · Jan 15, 2026 · 12 weeks