Welcome to the course. Here's what you're getting:

6 chapters, 18 lessons covering the full NLP pipeline stack — from raw text preprocessing to production model serving with drift detection.

Course Roadmap

1. Foundations of NLP — Preprocessing, tokenization, and the text pipeline

2. Word Representations & Embeddings — Word2Vec, GloVe, FastText, and custom embedding pipelines

3. Sequence Models & Attention — RNNs, LSTMs, attention mechanisms, and the Transformer from scratch

4. Named Entity Recognition — Rule-based, CRF, neural NER, and fine-tuning BERT

5. Sentiment Analysis & Text Classification — Classical ML, transfer learning, multi-label classification

6. Production Deployment — ONNX optimization, quantization, FastAPI serving, monitoring & MLOps

How to Get the Most Out of This

• Lessons are sequential — complete them in order

• Every lesson includes production-ready code you can copy into your projects

• Certificate awarded on completion

• Drop questions in the Community Chat

Let's build something that ships.

I spent 4 years building computer vision systems before realizing something: the best ML engineers aren't specialists — they're pipeline engineers.

Every production ML system I shipped had the same bottlenecks: data preprocessing, model optimization, serving infrastructure, and monitoring. The model architecture was maybe 10% of the work.

When I started applying these same pipeline patterns to NLP, everything clicked. Tokenization pipelines. Embedding strategies. ONNX export. Dynamic batching. Drift detection. The principles are universal.

So I built the course I wish I had — 18 hands-on lessons covering:

• Text preprocessing & tokenization deep dives

• Building custom embedding pipelines

• Transformers from scratch (not just using HuggingFace — understanding the architecture)

• Fine-tuning BERT for NER and classification

• Model optimization: quantization, distillation, ONNX

• Production serving with FastAPI + dynamic batching

• Monitoring, drift detection, and automated retraining

Every lesson includes production-ready Python code. No toy examples.

If you're an ML engineer, data scientist, or software developer who wants to ship NLP systems that actually work in production — this is for you.

$286/month with a 1-day free trial. Start building today.