📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Natural Language Generation Course, from foundations to a certified capstone project.
Outline
Analyze the fundamental concepts of linguistics and their application to natural language processing and generation • Develop a comprehensive understanding of the NLP pipeline, including text processing, tokenization, and feature extraction • Evaluate the strengths and limitations of rule-based and machine learning approaches to natural language generation
Outline
Implement text preprocessing techniques, including tokenization, stemming, and lemmatization, to prepare text data for NLP tasks • Design and develop feature extraction methods, such as bag-of-words and term frequency-inverse document frequency, to represent text data in a numerical format • Configure and optimize text preprocessing pipelines using popular NLP libraries and frameworks
Outline
Develop and apply statistical models, such as n-gram and hidden Markov models, to natural language processing tasks • Analyze and evaluate the performance of classical NLP models, including their strengths and limitations • Implement and optimize statistical methods, such as maximum likelihood estimation and Bayesian inference, for NLP tasks
Outline
Design and develop deep learning architectures, including recurrent neural networks and long short-term memory networks, for natural language generation tasks • Implement and optimize deep learning models using popular frameworks, such as TensorFlow and PyTorch • Evaluate the performance of deep learning models for NLG tasks, including their ability to generate coherent and contextually relevant text
Outline
Implement and optimize transformer-based architectures, including BERT and RoBERTa, for natural language generation tasks • Develop and apply attention mechanisms, including self-attention and cross-attention, to improve the performance of NLG models • Analyze and evaluate the performance of large language models, including their ability to generate coherent and contextually relevant text
Outline
Develop and apply evaluation metrics, including perplexity and BLEU score, to assess the performance of NLG models • Implement and optimize fine-tuning techniques, including transfer learning and domain adaptation, to improve the performance of pre-trained NLG models • Configure and optimize hyperparameters, including learning rate and batch size, to improve the performance of NLG models
Outline
Design and develop production-ready NLP systems, including APIs and microservices, for natural language generation tasks • Implement and optimize deployment strategies, including containerization and cloud deployment, for NLP systems • Evaluate and ensure the scalability, reliability, and security of production NLP systems
Earn government-registered certification in Natural Language Generation Course
e-Certificate and e-Marksheet issued on successful completion.
