Natural Language Processing (NLP)
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10 lessons3 levels1 capstone project
Understand language as data and build NLP systems across tokenization, embeddings, classification, sequence modeling, transformers, information extraction and modern pretrained models.
BASIC
- Introduction to Natural Language Processing (Medium)
Clear introduction to entities, relations, concepts and semantic structure. - Natural Language Processing with TensorFlow 2 (Long)
Practical end-to-end NLP with neural models and TensorFlow. - Natural Language Processing with spaCy & Python (Long)
Applied NLP pipelines for real-world text problems.
INTERMEDIATE
- Star Schema / Dimensional Modeling
Apply Natural Language Processing (NLP) through the adjacent capability: Data Engineering. - Data Engineer Roadmap 2026 — Codebasics
Apply Natural Language Processing (NLP) through the adjacent capability: Data Engineering. - Image Classification with CNNs — Stanford CS231N
Apply Natural Language Processing (NLP) through the adjacent capability: Computer Vision.
ADVANCED
- End-to-End Databricks Data Engineering Project
Extend Natural Language Processing (NLP) into senior/production practice through Data Engineering. - Create a Lakehouse, Ingest Data and Build a Report
Extend Natural Language Processing (NLP) into senior/production practice through Data Modeling, Warehousing & Lakehouse. - OpenCV Course with Python — freeCodeCamp
Extend Natural Language Processing (NLP) into senior/production practice through Computer Vision.