Outline

Implement text preprocessing techniques, including tokenization, stemming, and lemmatization • Design and develop feature engineering pipelines for NLP tasks, including bag-of-words and term frequency-inverse document frequency (TF-IDF) • Configure and optimize text preprocessing workflows for improved model performance

Outline

Develop and apply classical NLP models, including n-gram models and Hidden Markov Models (HMMs) • Analyze and evaluate the performance of statistical methods, including maximum likelihood estimation and Bayesian inference • Implement and optimize classical NLP algorithms, including Viterbi algorithm and forward-backward algorithm

Outline

Design and develop deep learning architectures for NLP tasks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) • Implement and optimize deep learning models, including word embeddings and attention mechanisms • Evaluate the performance of deep learning architectures for NLP tasks, including language modeling and text classification

Outline

Implement and optimize Transformer architectures, including BERT and RoBERTa • Develop and apply Large Language Models (LLMs) for NLP tasks, including language translation and text generation • Analyze and evaluate the role of attention mechanisms in improving model performance and interpretability

Outline

Evaluate the performance of NLP models using metrics, including accuracy, precision, and recall • Fine-tune and optimize NLP models using techniques, including hyperparameter tuning and model pruning • Develop and apply model interpretability techniques, including feature importance and partial dependence plots

Outline

Design and develop production-ready NLP systems, including data pipelines and model serving • Implement and deploy NLP APIs using frameworks, including Flask and Django • Configure and optimize NLP systems for scalability and reliability, including containerization and orchestration