Natural Language Processing

A progressive learning path from text representation and classical NLP through recurrent neural networks, attention, Transformers, RAG, and production engineering.

01 Foundations How does text become data?

02 Classical NLP How far can sparse features take us?

03 Neural NLP How do models learn sequence and context?

04 Transformers How does attention change NLP?

05 Applications How do we solve real language tasks?

06 Engineering How do we evaluate and operate NLP?

07 Capstone How does it all fit together?

00 How to use this guide

Work through the modules in order the first time. Each module starts with the problem being solved, then builds the mental model, mechanics, use cases, code where useful, practical pitfalls, and checkpoint questions.

Do not treat the sequence as “old technology replaced by new technology.” A recurring objective is to understand when a simpler method such as TF-IDF + Logistic Regression is sufficient and when richer context-sensitive approaches justify their added complexity.

01 Foundations

Establish the language-data mental model before choosing algorithms.

1. NLP Mental Model: What Problem Are We Solving?

Build the vocabulary needed to understand every later NLP architecture.

Basic 3 topics 3 checks

2. Text Preprocessing and Tokenization

Learn how raw text becomes model-ready units.

Basic 3 topics 2 checks

02 Classical NLP

Build strong, interpretable baselines with sparse representations.

3. Bag of Words, N-grams and TF-IDF

Master the strongest classical baseline before moving to neural NLP.

Basic → Intermediate 4 topics 2 checks

03 Neural NLP

Move from fixed vectors to learned representations and sequence models.

4. From Sparse Vectors to Embeddings

Understand the representation change that enables modern NLP.

Intermediate 3 topics 2 checks

5. ANN vs RNN: Why Sequence Changes the Architecture

Connect classical fixed-vector modeling to recurrent sequence processing.

Intermediate 3 topics 2 checks

6. Simple RNN: Forward Pass, Hidden State and Training

Understand recurrent computation before LSTM, attention and Transformers.

Intermediate 3 topics 2 checks

7. LSTM and GRU: Better Memory

Understand how gating addresses limitations of vanilla RNNs.

Intermediate 4 topics 2 checks

8. Bidirectional RNNs and Encoder–Decoder Models

Bridge recurrent models to translation and later attention.

Intermediate → Advanced 3 topics 2 checks

04 Transformers

Learn attention, Transformer architecture, pretraining, and fine-tuning.

9. Attention: Let the Model Look Back Selectively

Understand the breakthrough that removes the single-context bottleneck.

Advanced 3 topics 2 checks

10. Transformer Architecture

Understand the architecture behind BERT, GPT and modern LLMs.

Advanced 3 topics 2 checks

11. Pretraining, Fine-tuning and Transfer Learning

Learn why modern NLP rarely starts from random weights.

Advanced 3 topics 2 checks

05 Applications

Connect model families to classification, NER, retrieval, generation, and RAG.

12. Core NLP Task Patterns

Connect architectures to real business outcomes.

Intermediate → Advanced 4 topics 2 checks

13. Embeddings, Vector Search and RAG

Connect NLP representation learning to enterprise LLM systems.

Advanced 3 topics 2 checks

06 Engineering

Evaluate, deploy, monitor, and govern NLP systems.

14. Evaluation: From Accuracy to Business Fitness

Prevent impressive demos from becoming poor production systems.

Intermediate → Advanced 3 topics 2 checks

15. Production NLP and MLOps

Understand what changes when an NLP model becomes a service.

Advanced 3 topics 2 checks

07 Capstone

Integrate the full learning path in an end-to-end banking use case.

16. Capstone: Banking Complaint Intelligence

Use one business problem to connect classical NLP, Transformers, retrieval and MLOps.

End-to-End 5 topics 3 checks