Advanced Generative Text Artificial Intelligence (Synchronous & Asynchronous e-Learning)
About This Course
The Advanced Generative Text Artificial Intelligence (AI) course is designed to provide learners with industry-relevant expertise in Natural Language Processing (NLP) and Large Language Models (LLMs). The course focuses on AI-driven text automation, business intelligence, and conversational AI. Participants will gain hands-on experience in developing, training, and optimising NLP and LLM models, covering key topics such as transformer architectures, attention mechanisms, embeddings, and transfer learning. These skills are essential for building scalable solutions for sentiment analysis, machine translation, chatbots, and automated text generation. Throughout the course, learners will explore the evolution, core methodologies, and real-world applications of NLP, including sentiment analysis, machine translation, and chatbot development. They will acquire hands-on proficiency in text processing techniques, such as tokenisation, embeddings, and variable types, which are crucial for building NLP solutions. The course emphasises the design, training, and evaluation of classification and regression models, incorporating advanced training techniques, optimisation strategies, and industry-standard evaluation metrics. By integrating theoretical knowledge with practical applications, learners will be prepared to develop scalable, AI-driven NLP solutions aligned with industry needs in data science, artificial intelligence, and machine learning. Additionally, the course focuses on equipping learners with expertise in LLMs, emphasising transformer architecture, attention mechanisms, and transfer learning. Participants will develop, optimise, and deploy AI-driven text generation models for applications such as chatbots and business-centric generative AI. They will gain practical skills in fine-tuning LLMs to ensure alignment with industry demands in AI-powered text automation.
What You'll Learn
• Design, train, and optimise machine learning and deep learning models for natural language understanding and generative AI, leveraging transformer architectures, attention mechanisms, embeddings, and transfer learning to enhance text-based applications.
• Evaluate and fine-tune NLP and LLM models using industry-standard performance metrics, optimisation strategies, and emerging advancements, ensuring effective deployment and alignment with business and AI industry needs.
• Apply foundational principles of LLMs, including transformer architectures, encoder-decoder structures, attention mechanisms, and transfer learning, to develop AI-driven text generation solutions.
• Design, develop, and deploy LLM-based applications for business tasks such as automated text generation, question-answering, and conversational AI, ensuring scalability and industry alignment.
• Utilise NLP architecture components such as text preprocessing, tokenisation, and embeddings to transform raw text data into structured representations for machine learning applications.
• Develop, train, and evaluate NLP models using machine learning and deep learning techniques, applying industry-standard metrics and optimisation strategies to enhance model performance and deployment readiness.
Entry Requirements
It is recommended that learners have at least one year of experience in Python programming, along with a foundational understanding of Machine Learning and Natural Language Processing.