engineers certified since 2021
Fluency in the language machines speak.
From transformer architectures to production-ready NLP pipelines. Real labs, real deployment, measurable outcomes — not another lecture series.
attention_weights[0]
lab content
completion rate
Numbers before
explanations.
Every metric is pulled from cohort exit surveys and LinkedIn salary data — independently verified, updated each quarter.
Completion Rate
vs. 15% industry avg for online ML courses
Avg. Salary Multiplier
median increase within 6 months of certification
Hours of Lab Content
hands-on pipelines, not passive video lectures
Hired Within 90 Days
of graduates actively job-seeking post-cert
Every card is a
proof point.
Explore All Tracks
satisfaction score
NLP Foundations & Tokenization
Text preprocessing, tokenizers, and vocabulary construction from scratch.
avg instructor rating
Transformer Architectures
Self-attention, positional encoding, BERT, GPT internals — built from tensors up.
median salary bump
Fine-Tuning & Alignment
LoRA, RLHF, instruction tuning — adapt foundation models to domain tasks.
to job-ready output
Prompt Engineering & RAG
Chain-of-thought, retrieval-augmented generation, and enterprise prompt patterns.
pass first-attempt cert
Production NLP Pipelines
Model serving, latency optimization, A/B testing, and MLOps for NLP at scale.
faster model iteration
Multimodal & Vision-Language
CLIP, LLaVA, image captioning, and cross-modal embedding spaces.
18% complete · Est. 52 hrs remaining
$34k
median salary increase
Transformer
Architectures
The flagship track. You will understand why attention works, not just how to call model.forward(). Intermediate engineers finish this track and build their own BERT variant from tensors.
Attention Is All You Need — dissected
Rebuild the original 2017 paper from first principles in PyTorch.
BERT pre-training from scratch
Masked LM + NSP objectives on a custom corpus. No shortcuts.
Fine-tuning for classification & NER
Adapter layers, frozen encoders, and token-level prediction heads.
Serving with ONNX + TensorRT
Export, quantize, and deploy at < 12ms p99 latency on CPU.
Proof from the
people who shipped.
Read All Stories →“I'd been writing SQL for 8 years. After the Transformer Architectures track, I shipped a named-entity recognition model to production in 11 weeks. The labs aren't exercises — they're real engineering problems.”
Marcus Delacroix
Senior ML Engineer · Palantir Technologies
“My PhD was in computational linguistics — I could parse a parse tree in my sleep. What I couldn't do was deploy anything. Tokenize taught me the gap between research and inference is entirely an engineering problem.”
Priya Venkataraman
NLP Research Engineer · Cohere
“We sent five data analysts through the Fine-Tuning & Alignment track before Q3. By Q4 they had fine-tuned a domain-specific BERT for contract extraction. Saved us $280k in vendor fees in the first quarter.”
Jordan Ashworth
Head of Data · Carta