AI Tutorials
Natural Language Processing
Master NLP from first principles — covering the full pipeline from tokenization, morphology, syntax, semantics, and discourse to text classification, NER, machine translation, QA, summarization, and production deployment with monitoring.
13 chapters · 371 min
From text preprocessing and phonology to transformers, QA, and production deployment
- Ch. 01Read →
Introduction & Text Preprocessing
The NLP pipeline, text normalization, tokenization, stemming, and lemmatization
beginner · 28 min
- Ch. 02Read →
Phonology & Speech Processing
Phonemes, automatic speech recognition, speech-to-text, and text-to-speech systems
intermediate · 25 min
- Ch. 03Read →
Morphology & Lexical Analysis
Word structure, morphemes, POS tagging, chunking, and lexical resources
beginner · 25 min
- Ch. 04Read →
Syntactic Analysis & Parsing
Context-free grammars, constituency trees, dependency parsing, and grammar checking
intermediate · 28 min
- Ch. 05Read →
Semantic Analysis
Word meaning, semantic similarity, word sense disambiguation, and semantic role labeling
intermediate · 28 min
- Ch. 06Read →
Discourse & Pragmatics
Coreference resolution, discourse structure, speech acts, sarcasm, and pragmatic interpretation
intermediate · 22 min
- Ch. 07Read →
Text Representation & Embeddings
From bag-of-words and TF-IDF to Word2Vec, GloVe, FastText, and contextual BERT embeddings
intermediate · 32 min
- Ch. 08Read →
Text Classification & Sentiment Analysis
Spam detection, sentiment analysis, topic classification, and zero-shot approaches
intermediate · 30 min
- Ch. 09Read →
Information Extraction & NER
Named entity recognition, relation extraction, event extraction, and knowledge base population
intermediate · 28 min
- Ch. 10Read →
Machine Translation
From statistical phrase-based MT to neural seq2seq, attention, and Transformer-based translation
advanced · 30 min
- Ch. 11Read →
Question Answering & Summarization
Extractive and abstractive QA, reading comprehension, RAG, and neural summarization
advanced · 32 min
- Ch. 12Read →
NLP Models: N-grams to Transformers
Language modeling from n-gram statistics to RNNs, LSTMs, attention, and large language models
advanced · 35 min
- Ch. 13Read →
NLP Evaluation & Deployment
Intrinsic and extrinsic evaluation, NLP benchmarks, production deployment, and model monitoring
intermediate · 28 min