💬Core AI
NLP
Natural Language Processing with transformers
Master Natural Language Processing from text preprocessing to building transformer-based models. Covers tokenization, embeddings, sentiment analysis, NER, and HuggingFace pipelines.
45 lessons
22 hrs
Intermediate
What you will learn
Build real-world projects from scratch
Write clean, production-ready code
Understand core concepts deeply with diagrams
Follow industry best practices
Get hands-on with code in every lesson
Access lifetime updates as the tech evolves
Curriculum
Module 1Text Preprocessing Fundamentals4 lessons
Module 2Regular Expressions for Text Extraction4 lessons
Regex Fundamentals — Literal Characters and Character Classes13 min
Extracting Real Structured Data — Emails, Phone Numbers, and Dates15 min
Capturing Groups and Lookaheads — Precise, Structured Extraction14 min
Combining Everything Into a Real Extraction Pipeline14 min
Module 3Bag-of-Words and TF-IDF3 lessons
Bag-of-Words — Turning Text Into Word Counts13 min
TF-IDF — Weighting Words by How Informative They Actually Are15 min
TF-IDF vs Embeddings — An Honest, Measured Comparison14 min
Module 4N-grams and Statistical Language Models3 lessons
N-grams — Capturing Short Sequences Instead of Single Words12 min
Building a Statistical Language Model From Bigram Counts15 min
Where This Approach Structurally Breaks Down13 min
Module 5Part-of-Speech Tagging2 lessons
What Part-of-Speech Tagging Actually Assigns12 min
A Real, Context-Aware Tagger — Fixing the Measured Failure14 min
Module 6Named Entity Recognition3 lessons
Why Regex Cannot Find Names, Places, and Organizations12 min
Building a Real NER Pipeline with spaCy14 min
Evaluating NER Correctly — Why Entity-Level F1, Not Token Accuracy14 min
Module 7Coreference Resolution3 lessons
Why Finding Entities Isn't Enough — The Pronoun Resolution Gap12 min
Building a Real Coreference Resolution Pipeline15 min
Where Coreference Resolution Still Struggles — Honest Limitations13 min
Module 8Dependency Parsing2 lessons
Why POS Tags Alone Cannot Answer 'Who Did What'12 min
Extracting Subject-Verb-Object Relationships for Real Use14 min
Module 9Text Classification Pipelines End to End2 lessons
Assembling a Complete Classification Pipeline15 min
Is TF-IDF Still Competitive? A Direct Measurement14 min
Module 10Using Hugging Face Pipelines Practically3 lessons
The Pipeline Abstraction — One Line, a Full Transformer Underneath12 min
Zero-Shot Classification — Classifying Without Any Training Data13 min
Translation Pipelines and Practical Model Selection Tradeoffs13 min
Module 11Fine-Tuning a Pretrained Transformer, the Practical Workflow2 lessons
From DL Module 32's Hand-Written Loop to Hugging Face's Trainer14 min
Practical Fine-Tuning Decisions — Learning Rate, Freezing, and Data Size14 min
31 total lessons across 11 modulesPreview Module 1 free