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課程簡介

Course Outline Training Proposal  

Day 1 - Introduction to AI and Python for Data Workflows

• Overview of artificial intelligence and machine learning landscape  

• Role of AI in modern data engineering  

• Python fundamentals refresher for AI applications

 • Working with data using pandas and NumPy  

• Introduction to APIs and JSON data handling

 • Mini exercise loading and transforming datasets  

Day 2 - Machine Learning Foundations for Practitioners

• Supervised and unsupervised learning concepts

 • Feature engineering and data preparation techniques

 • Model training basics using scikit-learn

 • Model evaluation and performance metrics

 • Introduction to model deployment concepts

 • Hands-on building a simple predictive model  

Day 3 - Introduction to LLMs and Prompt Engineering

• Understanding large language models and how they work  

• Tokenization, context windows, and limitations

 • Prompt design principles and techniques  

• Zero-shot and few-shot prompting

 • Prompt evaluation and iteration strategies

 • Hands-on prompt engineering exercises  

Day 4-  Building AI Applications with LLMs

• Using LLM APIs in Python

 • Structured outputs and function calling concepts

• Building chat-based and task-based applications

• Introduction to retrieval augmented generation  

• Connecting LLMs with external data sources 

• Mini project building a simple AI assistant 

Day 5 - Productionizing AI Solutions

• Designing scalable AI workflows  

• Integrating AI into data pipelines  

• Monitoring and improving model performance  

• Cost optimization and API usage strategies

 • Security and responsible AI considerations  

• Final project building an end-to-end AI solution  

 35 小時

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