A rigorous, hands-on journey through Natural Language Processing — from tokenization to transformers, from sentiment to search.
Every concept built carefully on the last. No shortcuts — just genuine, transferable understanding.
Understand the architecture that changed everything. Attention mechanisms, positional encoding, encoder-decoder structure — dissected and explained with working code.
How text becomes numbers. BPE, WordPiece, word2vec, and contextual embeddings.
Identify people, places, and things in text. Build NER systems from scratch.
Classify emotions, detect intent, and understand what text really means.
Pre-training, fine-tuning, and prompt engineering for modern LLMs.
Build retrieval-augmented generation pipelines that ground AI responses in real documents. Vector databases, cosine similarity, re-ranking.
Seq2Seq, attention-based translation, and fine-tuning multilingual models for low-resource languages.
A logical, sequential curriculum. Each module earns its place. Each project is deployable.
Syntax, semantics, pragmatics — what makes language hard for machines
Cleaning, normalizing, BPE, SentencePiece, subword vocabularies
Word2Vec, GloVe, FastText, contextual embeddings
Building intuition before transformers. Where they shine, where they fail.
Attention is all you need — unpacked line by line
Pre-training, masked LM, fine-tuning for classification & NER
Auto-regressive generation, prompt engineering, parameter-efficient fine-tuning
Embeddings, FAISS, Pinecone, and RAG from scratch
FastAPI, HuggingFace Hub, serving models at scale
End-to-end RAG-powered document Q&A from ingestion to deployment
30-day refund · No subscription
The transformer module is worth the entire price of the course. I finally understood attention after spending months confused. The code walkthroughs are exceptionally clear.
I used the capstone project as a portfolio piece and got hired at a startup building legal document analysis tools. The real-world focus of this course is unmatched.
I'm a linguist who wanted to understand the computational side of my field. This course bridges the gap perfectly — rigorous but never condescending about technical details.
Maya spent eight years as a research scientist at the Allen Institute for AI, focusing on reading comprehension models and low-resource language translation. She's co-authored over 30 peer-reviewed papers on NLP.
Her teaching philosophy: understanding comes before application. Every concept in this course is explained at the mathematical level before a single line of code is written.
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