Chapter 1
Introduction to NLP
Business applications of NLP (Chatbots, Summarization, Search)
medium • 1 min read
Text processing pipeline (Tokenization, Stop words, Stemming, Lemmatization)
medium • 2 min read
Regular expressions for text extraction
medium • 3 min read
Parts-of-Speech (POS) tagging and Named Entity Recognition (NER)
medium • 4 min read
Overview of NLP libraries (NLTK, SpaCy)
medium • 5 min read
Bag of Words (BoW) and Document-Term Matrix (DTM)
medium • 1 min read
Term Frequency-Inverse Document Frequency (TF-IDF)
medium • 2 min read
Word Embeddings concept (Word2Vec, GloVe)
medium • 3 min read
Properties of dense vectors vs sparse vectors
medium • 4 min read
Cosine similarity for document matching
medium • 5 min read
Lexicon-based approaches for sentiment analysis (VADER)
medium • 1 min read
Supervised approaches to sentiment classification
medium • 2 min read
Aspect-based sentiment analysis for product reviews
medium • 3 min read
Latent Dirichlet Allocation (LDA) for topic modeling
medium • 4 min read
Interpreting and visualizing topics (pyLDAvis)
medium • 5 min read
Chapter 4
Advanced NLP Architectures
Using RNNs and LSTMs for text classification
medium • 1 min read
Sequence-to-Sequence models and Attention mechanism
medium • 2 min read
Introduction to Transformers architecture
medium • 3 min read
Understanding Pre-trained models (BERT, GPT, RoBERTa)
medium • 4 min read
Fine-tuning BERT for business classification tasks
medium • 5 min read
Large Language Models (LLMs) in business operations
medium • 1 min read
Prompt Engineering basics and best practices
medium • 2 min read
Retrieval-Augmented Generation (RAG) for internal knowledge bases
medium • 3 min read
Building enterprise chatbots using LangChain
medium • 4 min read
Ethical usage, hallucinations, and bias in Generative AI
medium • 5 min read