AI Application Development with Large Language Models (LLM)
About This Course
Dive into the innovative world of AI Application Development with Large Language Models (LLM) in this comprehensive course. Designed for those looking to enhance their digital marketing expertise, the course focuses on developing practical skills for formulating and budgeting effective social media strategies. You'll learn how to align these strategies with organizational standards and market needs, ensuring a robust online presence. The course emphasizes the use of AI and large language models in analyzing customer behavior, providing insights into creating impactful digital content that resonates with your audience.
In addition to understanding the operational aspects, the course provides a deeper exploration into the evaluation of digital marketing technologies on Meta platforms. This includes an in-depth look at tools and techniques for managing social media, understanding customer behavior, and planning communication effectively. The course also delves into the intricacies of synthesizing market trends, incorporating a keen understanding of privacy and copyright laws, and how they influence social media marketing strategies. By the end of the course, participants will be equipped with the knowledge and skills to leverage AI in crafting cutting-edge digital marketing strategies that drive engagement and business growth.
What You'll Learn
- Analyze AI applications using large language models (LLM)
- Establish LLM algorithm methodologies with word embedding and vector databases
- Identify and evaluate strengths, limitations of LLM applications, and effectiveness in various tasks.
- Assess LLM application feasibility in product development using function calling and langchain.
- Evaluate and finetune LLM models for application improvement
Course Outline:
Topic 1 Fundamental of Large Language Model (LLM)
- What are LLMs
- Applications and use cases of LLMs
- Create a custom GPTs application from scratch
- Overview of Transformer architecture
- Self-attention mechanism
- Comparison of popular LLM Models
Topic 2 Word Embedding and Vector Databases
- Word embedding
- Similarity and correlation of words
- Vector databases
Topic 3 Evaluating LLM Tasks
- NER
- Translation
- Summarization
- Text Generation
- Question Answering
- Retrieval Augmented Generation (RAG)
Topic 4 Introduction to Function Calling and Langchain
- OpenAI Function Calling
- Langchain Components, Tools and Agents
Topic 5 Fine Tuning LLM Models
- Why fine tuning LLM models
- Training and evaluation of fine-tuned LLM models
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target Age Group: 21 to 65 years old