NLP Study Plan
A comprehensive 14-week curriculum covering natural language processing from text preprocessing and word embeddings to transformers, machine translation, and advanced NLP.
Recommended Study Path
Prerequisites
Foundations Study Plan
- Python Foundations
- NumPy & Data Wrangling
- Mathematical Foundations
Complete the Foundations study plan first →
Fundamentals
Weeks 1-4
- Ch 1-2: Intro & Preprocessing
- Ch 3-4: Representation & Embeddings
Tokenization, TF-IDF, Word2Vec, GloVe
Deep NLP
Weeks 5-10
- Ch 5-6: Sequences & Attention
- Ch 7-10: Classification, NER & MT
RNNs, Transformers, BERT, translation
Advanced
Weeks 11-14
- Ch 11-12: QA & Summarization
- Ch 13: Zero-Shot, Ethics & Frontiers
Retrieval, generation, multimodal NLP
All Chapters
Introduction to NLP
What is NLP, history of natural language processing, core tasks, text as data, challenges in language understanding, and real-world applications.
Text Preprocessing
Tokenization techniques, stemming and lemmatization, stopword removal, regular expressions for text, normalization, and text encoding strategies.
Text Representation
Bag of words model, TF-IDF weighting, N-gram language models, one-hot encoding, co-occurrence matrices, and dimensionality reduction techniques.
Word Embeddings
Word2Vec CBOW and Skip-gram architectures, GloVe embeddings, FastText subword representations, embedding evaluation, and subword embeddings.
Sequence Models
Recurrent neural networks, LSTMs for long-range dependencies, GRUs, bidirectional RNNs, sequence-to-sequence models, and encoder-decoder architectures.
Attention Mechanisms
Attention basics and intuition, self-attention, multi-head attention, positional encoding, the Transformer architecture, and attention variants.
Text Classification
Sentiment analysis, document classification, multi-label classification, CNNs for text, fine-tuning pretrained models, and evaluation metrics.
Named Entity Recognition
NER approaches and architectures, BIO tagging scheme, CRF layers for sequence labeling, SpaCy NER, evaluation metrics, and custom NER training.
Language Modeling
Statistical language models, neural language models, perplexity metric, BERT architecture, GPT architecture, and masked vs causal language modeling.
Machine Translation
Parallel corpora, word alignment, sequence-to-sequence with attention, Transformer-based MT, BLEU score evaluation, and multilingual models.
QA & Information Retrieval
Extractive question answering, generative QA, passage retrieval, dense retrieval methods, reading comprehension, and open-domain QA systems.
Text Generation & Summarization
Extractive summarization, abstractive summarization, beam search decoding, nucleus sampling, controlling generation, and evaluation metrics.
Advanced NLP Topics
Zero-shot learning, few-shot learning, prompt engineering, multimodal NLP, ethics and bias in NLP, and research frontiers.
Timed Tests
Practice Problem Sets
Sharpen your skills with coding challenges and system design problems.
Curriculum designed to take you from NLP fundamentals to advanced text understanding and generation systems.