NLP & Text Analytics

Master essential text preprocessing techniques including tokenization, stemming, lemmatization, and text normalization to prepare raw text for NLP tasks.

Understand and implement word embeddings that capture semantic meaning using Word2Vec, GloVe, and FastText to represent words as dense vectors.

Learn traditional text representation methods including bag of words, TF-IDF, and n-gram features for document classification and information retrieval.

Master text classification with Naive Bayes, SVM, and ensemble methods for spam detection, sentiment analysis, and topic classification.

Learn to identify and classify named entities like people, organizations, locations, and dates in text using rule-based and machine learning approaches.

Master sentiment analysis techniques from lexicon-based approaches to machine learning models for analyzing opinions, emotions, and attitudes in text.

Discover hidden themes in document collections using Latent Dirichlet Allocation (LDA) and other unsupervised topic modeling techniques.

Master encoder-decoder architectures for machine translation, text summarization, and other sequence transformation tasks.

Understand the transformer architecture and BERT that revolutionized NLP with self-attention and pre-training on massive text corpora.

Build intelligent QA systems using extractive and generative approaches with BERT-based models and retrieval-augmented generation.

Learn extractive and abstractive summarization techniques to automatically condense documents while preserving essential information.

Master neural language models for text generation, from n-gram models to GPT-style transformers with various decoding strategies.

Build NLP systems that work across multiple languages using cross-lingual embeddings, multilingual transformers, and machine translation.

Learn to build production-ready NLP systems with proper evaluation, deployment strategies, monitoring, and ethical considerations.