Generative AI Models for Natural Language (NLP & NLU)

Yes. Since this is an intermediate GenAI NLP course, familiarity with Python and PyTorch is required. Basic understanding of machine learning and neural network concepts is also recommended.

Learners will gain job-ready skills in NLP & NLU, including feature representation using embeddings, building and training language models, document classification, sequence-to-sequence modeling, and text analysis. You will also practice implementing these skills in hands-on generative AI labs to prepare for real-world projects.

NLP, or natural language processing, focuses on processing and analyzing text data, such as language modeling, text generation, and classification. NLU, or Natural Language Understanding, involves interpreting and extracting meaning from text, enabling tasks like document categorization, translation, and semantic analysis. This course integrates both to build practical GenAI NLP skills.

Yes. The NLP course teaches both CBOW and Skip-Gram Word2Vec models and demonstrates how to integrate pre-trained embeddings into NLP tasks using PyTorch.

These techniques convert words into numerical features that can be processed by machine learning and deep learning models. One-hot encoding and bag-of-words represent words in simple vector formats, while embeddings like Word2Vec capture semantic meaning, which improves model performance for NLP applications.

N-gram models analyze sequences of words to predict the next word or classify text. In this course, N-grams are implemented with neural networks in PyTorch to create simple language models for tasks like text generation and document classification.

Unlike many generic NLP courses online, this program focuses on hands-on projects with PyTorch, practical labs, and real-world applications of generative AI models. It combines both theory and implementation, helping you build job-ready skills in NLP and NLU.

Yes. The course covers encoder-decoder RNN models, including training and inference, and applies them to translation and sequence transformation tasks, giving learners hands-on experience in building GenAI NLP models.

You will implement encoder-decoder RNNs in PyTorch, train them on sample datasets, and use them for language translation tasks. Labs guide you step-by-step to apply sequence-to-sequence learning for text generation.

Yes. The course introduces metrics such as BLEU to evaluate the quality of generated text, helping learners assess the performance of their NLP and NLU models.

Yes. The course features multiple hands-on Generative AI labs, including document classification with Torchtext, sequence-to-sequence modeling, Word2Vec integration, and training neural networks for language modeling and translation.

Learners will build neural language models, integrate pre-trained embeddings, perform text classification, generate text with sequence-to-sequence models, and evaluate model performance using BLEU scores. These projects provide practical experience to include in a portfolio.

By completing the hands-on labs, building practical models, and learning industry-standard NLP and NLU techniques, learners gain experience and skills that employers look for in AI and data science roles focused on natural language understanding and generative AI modeling.

Yes. Learners receive an IBM Certificate upon completion, which can be showcased to potential employers.

Yes. This generative AI modeling course equips learners with practical experience in NLP and NLU, hands-on exposure to GenAI NLP modeling, and knowledge of PyTorch-based implementations, providing a strong foundation for careers in AI, data science, and natural language applications.