Natural Language Processing and Text Analytics
Description
Comprehensive study of NLP techniques, language models, and text processing for understanding and generating human language.
Learning Objectives
Learners will master text preprocessing and feature extraction techniques, implement various NLP algorithms for classification and sentiment analysis, understand language models and transformer architectures, develop chatbots and conversational AI systems, and apply NLP techniques to real-world text analytics problems.
Topics (9)
Word embedding techniques including Word2Vec (CBOW and Skip-gram), GloVe, FastText, and contextual embeddings for representing words and documents in vector space for semantic analysis.
Text classification techniques including naive Bayes, SVM for text, deep learning approaches, sentiment analysis methods, emotion detection, and evaluation metrics for text classification tasks.
Question answering systems including extractive and abstractive QA, reading comprehension models, knowledge-based QA, and evaluation metrics for QA systems using modern transformer architectures.
Text summarization techniques including extractive and abstractive summarization, automatic text generation, creative writing AI, and evaluation metrics for generated text quality and relevance.
Fundamental text processing techniques including tokenization, normalization, stemming, lemmatization, stop word removal, n-grams, TF-IDF vectorization, and traditional feature extraction methods for NLP tasks.
Modern language models including transformer architecture, attention mechanisms, BERT, GPT series, T5, and other pre-trained language models for understanding and generating natural language.
Information extraction techniques including named entity recognition, relation extraction, event extraction, coreference resolution, and knowledge graph construction from unstructured text data.
Machine translation techniques including statistical machine translation, neural machine translation, sequence-to-sequence models, attention mechanisms, and multilingual NLP challenges and solutions.
Conversational AI development including intent recognition, entity extraction, dialogue management, response generation, chatbot frameworks, and evaluation of conversational systems.