Specialized Topics
Natural Language Processing — Teaching Computers to Read
NLP enables computers to understand, interpret, and generate human language — bridging the gap between raw text and actionable insights.
- Text Preprocessing — tokenization, stemming, and lemmatization clean and normalize raw text
- TF-IDF and Bag of Words — simple but effective vectorization methods for text classification
- Word Embeddings — Word2Vec and GloVe capture semantic relationships between words in dense vector space
"Language is the house of being." — Martin Heidegger
NLP Fundamentals — Complete Guide
Natural Language Processing enables computers to understand and generate human language.
Mathematical Foundations
TF-IDF Formula
where:
(term frequency)
(inverse document frequency)
Cosine Similarity (for embeddings)
Word2Vec Skip-gram Objective
NLP Preprocessing Pipeline
Bag of Words and TF-IDF
Word Embeddings Space
Word Embeddings
N-grams and Local Word Order
Text Classification
Key Takeaways
What to Learn Next
-> Transformers Learn the self-attention architecture that revolutionized NLP and powers modern AI.
-> BERT Master encoder-only transformers for text classification, NER, and question answering.
-> GPT Architecture Understand decoder-only transformers that power autoregressive text generation.
-> Naive Bayes Learn the simple probabilistic classifier often used as a strong NLP baseline.
-> RNN and LSTM Explore sequential models that were the dominant NLP approach before transformers.
-> GANs Discover generative adversarial networks for text generation and style transfer.