Outline

Implement text preprocessing techniques such as tokenization, stemming, and lemmatization • Design and develop feature engineering pipelines for NLP tasks • Configure and optimize text preprocessing workflows for improved model performance

Outline

Apply classical NLP models such as n-gram models and Hidden Markov Models to real-world problems • Develop and evaluate statistical methods for NLP tasks such as sentiment analysis and topic modeling • Analyze and compare the performance of different classical NLP models and statistical methods

Outline

Design and implement deep learning architectures such as recurrent neural networks and transformers for generative AI tasks • Develop and train generative models such as language models and text generators • Evaluate and optimize the performance of deep learning architectures for generative AI tasks

Outline

Implement and apply transformer architectures such as BERT and RoBERTa to NLP tasks • Develop and evaluate large language models such as LLaMA and PaLM • Analyze and compare the performance of different transformer architectures and attention mechanisms

Outline

Evaluate and compare the performance of different NLP models using metrics such as accuracy and F1-score • Fine-tune and optimize NLP models for improved performance on specific tasks • Develop and implement model optimization techniques such as hyperparameter tuning and model pruning

Outline

Design and develop production-ready NLP systems and APIs • Deploy and manage NLP models in cloud-based environments such as AWS and Google Cloud • Configure and optimize NLP systems for scalability and reliability